{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:50.582807Z",
     "iopub.status.busy": "2025-06-01T12:49:50.582541Z",
     "iopub.status.idle": "2025-06-01T12:49:50.595132Z",
     "shell.execute_reply": "2025-06-01T12:49:50.594716Z",
     "shell.execute_reply.started": "2025-06-01T12:49:50.582790Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.040680Z",
     "start_time": "2024-05-16T13:58:19.777010Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:50.596678Z",
     "iopub.status.busy": "2025-06-01T12:49:50.596561Z",
     "iopub.status.idle": "2025-06-01T12:49:53.296961Z",
     "shell.execute_reply": "2025-06-01T12:49:53.296398Z",
     "shell.execute_reply.started": "2025-06-01T12:49:50.596665Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from preference_data_preparation_diff import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from suno_utils.utils.s3 import download_s3_files\n",
    "from suno_utils.utils.text import read_json, read_jsonl, write_json, write_jsonl\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:53.298993Z",
     "iopub.status.busy": "2025-06-01T12:49:53.298864Z",
     "iopub.status.idle": "2025-06-01T12:49:53.318478Z",
     "shell.execute_reply": "2025-06-01T12:49:53.318038Z",
     "shell.execute_reply.started": "2025-06-01T12:49:53.298979Z"
    }
   },
   "outputs": [],
   "source": [
    "OUT_DATA_DIR = \"/app/suno/data/dpo/diffv2_v1_t22/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "NPZ_DIR = \"/app/suno/data/dpo/diff2_v1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:53.962528Z",
     "start_time": "2024-05-16T13:58:21.105919Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:53.320431Z",
     "iopub.status.busy": "2025-06-01T12:49:53.320304Z",
     "iopub.status.idle": "2025-06-01T12:49:54.180831Z",
     "shell.execute_reply": "2025-06-01T12:49:54.180253Z",
     "shell.execute_reply.started": "2025-06-01T12:49:53.320418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (34412, 89)\n",
      "unique users 12780\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    \"/home/tony/Data/Preference/up_diff2_v1/interesting_clips_upv2_u1_20250417_full.pkl\"\n",
    ")  # , engine='python')\n",
    "print(\"Preference data shape\", df.shape)\n",
    "print(\"unique users\", df[\"user_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:54.182686Z",
     "iopub.status.busy": "2025-06-01T12:49:54.182558Z",
     "iopub.status.idle": "2025-06-01T12:49:54.199827Z",
     "shell.execute_reply": "2025-06-01T12:49:54.199355Z",
     "shell.execute_reply.started": "2025-06-01T12:49:54.182671Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    31743\n",
      "True      2669\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"is_public\"].value_counts())\n",
    "# # remove public for now cause fucking users\n",
    "# df = df[~df[\"is_public\"]]\n",
    "# print(df[\"is_public\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:54.201559Z",
     "iopub.status.busy": "2025-06-01T12:49:54.201430Z",
     "iopub.status.idle": "2025-06-01T12:49:54.235337Z",
     "shell.execute_reply": "2025-06-01T12:49:54.234891Z",
     "shell.execute_reply.started": "2025-06-01T12:49:54.201545Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"upsample_clip_id\"] = df[\"metadata\"].apply(lambda x: x.get(\"upsample_clip_id\", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.199480Z",
     "start_time": "2024-05-16T13:58:53.963687Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:54.236084Z",
     "iopub.status.busy": "2025-06-01T12:49:54.235948Z",
     "iopub.status.idle": "2025-06-01T12:49:56.792471Z",
     "shell.execute_reply": "2025-06-01T12:49:56.791891Z",
     "shell.execute_reply.started": "2025-06-01T12:49:54.236070Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72369\n",
      "23981\n",
      "48388\n",
      "pre-downloaded df (34412, 90)\n",
      "downloaded df (34406, 90)\n",
      "vae downloaded df (34406, 90)\n"
     ]
    }
   ],
   "source": [
    "all_converted_paths = os.listdir(NPZ_DIR)\n",
    "print(len(all_converted_paths))\n",
    "\n",
    "converted_paths = set(\n",
    "    [f.replace(\".npz\", \"\") for f in all_converted_paths if \"vae\" not in f]\n",
    ")\n",
    "print(len(converted_paths))\n",
    "vae_converted_paths = set(\n",
    "    [f.replace(\"_vae.npz\", \"\") for f in all_converted_paths if \"vae\" in f]\n",
    ")\n",
    "print(len(vae_converted_paths))\n",
    "\n",
    "print(\"pre-downloaded df\", df.shape)\n",
    "df[df[\"upsample_clip_id\"].isin(converted_paths)].shape\n",
    "df = df[df[\"upsample_clip_id\"].isin(converted_paths)].copy()\n",
    "print(\"downloaded df\", df.shape)\n",
    "df[df[\"s3_id\"].isin(vae_converted_paths)].shape\n",
    "df = df[df[\"s3_id\"].isin(vae_converted_paths)].copy()\n",
    "print(\"vae downloaded df\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.467253Z",
     "start_time": "2024-05-16T13:58:56.207647Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:56.793207Z",
     "iopub.status.busy": "2025-06-01T12:49:56.793052Z",
     "iopub.status.idle": "2025-06-01T12:49:56.818221Z",
     "shell.execute_reply": "2025-06-01T12:49:56.817785Z",
     "shell.execute_reply.started": "2025-06-01T12:49:56.793192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    34406\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_up\"] = df[\"model_name\"].str.contains(\"up\")\n",
    "df[\"is_up\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LET's do the data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.592883Z",
     "start_time": "2024-05-16T13:58:56.470781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:56.818874Z",
     "iopub.status.busy": "2025-06-01T12:49:56.818726Z",
     "iopub.status.idle": "2025-06-01T12:49:56.847782Z",
     "shell.execute_reply": "2025-06-01T12:49:56.847310Z",
     "shell.execute_reply.started": "2025-06-01T12:49:56.818859Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name       \n",
      "False       chirp-v4-up-u-d-2    17203\n",
      "True        chirp-v4-up-u-d-2    17203\n",
      "Name: count, dtype: int64\n",
      "(34406, 91)\n",
      "(34406, 91)\n"
     ]
    }
   ],
   "source": [
    "## for 13b this is easy for now\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "print(df.shape)\n",
    "df = df[df[\"model_name\"].isin([\"chirp-v4-up-u-d-2\"])]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.909539Z",
     "start_time": "2024-05-16T13:58:56.595736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:56.848403Z",
     "iopub.status.busy": "2025-06-01T12:49:56.848263Z",
     "iopub.status.idle": "2025-06-01T12:49:56.888125Z",
     "shell.execute_reply": "2025-06-01T12:49:56.887648Z",
     "shell.execute_reply.started": "2025-06-01T12:49:56.848389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34406, 91)\n",
      "(34406, 91)\n",
      "preference  model_name       \n",
      "False       chirp-v4-up-u-d-2    17203\n",
      "True        chirp-v4-up-u-d-2    17203\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "assert df.shape[0] == df[\"request_id\"].nunique() * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:56.888763Z",
     "iopub.status.busy": "2025-06-01T12:49:56.888624Z",
     "iopub.status.idle": "2025-06-01T12:49:58.679818Z",
     "shell.execute_reply": "2025-06-01T12:49:58.679242Z",
     "shell.execute_reply.started": "2025-06-01T12:49:56.888748Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pair quality scores: 17206\n",
      "Total unpacked pair quality scores: 198116\n"
     ]
    }
   ],
   "source": [
    "with open(\"/home/tony/Data/Preference/up_diff2_v1/full_pair_quality.json\", \"r\") as file:\n",
    "    full_pair_quality = json.load(file)\n",
    "print(\"Total pair quality scores:\", len(full_pair_quality))\n",
    "\n",
    "unpacked_pair_quality = {}\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    for clip_id, pair_quality in pairs_of_qualities.items():\n",
    "        unpacked_pair_quality[clip_id] = pair_quality\n",
    "print(\"Total unpacked pair quality scores:\", len(unpacked_pair_quality))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:49:58.680559Z",
     "iopub.status.busy": "2025-06-01T12:49:58.680403Z",
     "iopub.status.idle": "2025-06-01T12:50:00.449744Z",
     "shell.execute_reply": "2025-06-01T12:50:00.449215Z",
     "shell.execute_reply.started": "2025-06-01T12:49:58.680543Z"
    }
   },
   "outputs": [],
   "source": [
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "spec_decay_diff = []\n",
    "last_spec_decay_diff = []\n",
    "clip_id_to_mean_ear_score = {}\n",
    "total_clip_ratios = []\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    mean_neg_scores = []\n",
    "    mean_pos_scores = []\n",
    "    pos_scores = []\n",
    "    neg_scores = []\n",
    "    neg_loudness = []\n",
    "    pos_loudness = []\n",
    "    neg_spec_decay = []\n",
    "    pos_spec_decay = []\n",
    "    neg_clip_id = None\n",
    "    pos_clip_id = None\n",
    "    for i, (clip_id, pair_quality) in enumerate(pairs_of_qualities.items()):\n",
    "        if i % 2 == 0:\n",
    "            mean_neg_scores.append(\n",
    "                np.mean(pair_quality[\"ear_v2_quality_scores\"] if pair_quality else 0)\n",
    "            )\n",
    "            neg_scores.extend(\n",
    "                pair_quality[\"ear_v2_quality_scores\"] if pair_quality else [0]\n",
    "            )\n",
    "            neg_loudness.append(\n",
    "                pair_quality[\"abs_loudness_factor\"] if pair_quality else 0\n",
    "            )\n",
    "            neg_spec_decay.append(pair_quality[\"spectrum_decay\"] if pair_quality else 0)\n",
    "            if neg_clip_id is None:\n",
    "                neg_clip_id = clip_id\n",
    "        if i % 2 == 1:\n",
    "            mean_pos_scores.append(\n",
    "                np.mean(pair_quality[\"ear_v2_quality_scores\"] if pair_quality else 0)\n",
    "            )\n",
    "            pos_scores.extend(\n",
    "                pair_quality[\"ear_v2_quality_scores\"] if pair_quality else [0]\n",
    "            )\n",
    "            pos_loudness.append(\n",
    "                pair_quality[\"abs_loudness_factor\"] if pair_quality else 0\n",
    "            )\n",
    "            pos_spec_decay.append(pair_quality[\"spectrum_decay\"] if pair_quality else 0)\n",
    "            if pos_clip_id is None:\n",
    "                pos_clip_id = clip_id\n",
    "    ratios = [\n",
    "        (pos - neg) / (pos + 0.0001)\n",
    "        for pos, neg in zip(mean_pos_scores, mean_neg_scores)\n",
    "    ]\n",
    "    pos_diffs = [\n",
    "        (pos - prev_pos) / (prev_pos + 0.0001)\n",
    "        for prev_pos, pos in zip(mean_pos_scores, mean_pos_scores[1:])\n",
    "    ]\n",
    "    neg_diffs = [\n",
    "        (neg - prev_neg) / (prev_neg + 0.0001)\n",
    "        for prev_neg, neg in zip(mean_neg_scores, mean_neg_scores[1:])\n",
    "    ]\n",
    "    loudness_diff.extend(\n",
    "        [\n",
    "            (pos_l - neg_l) / (pos_l + neg_l + 0.0001)\n",
    "            for (pos_l, neg_l) in zip(pos_loudness, neg_loudness)\n",
    "        ]\n",
    "    )\n",
    "    spec_decay_diff.extend(\n",
    "        [\n",
    "            (pos_s - neg_s) / (pos_s + neg_s + 0.0001)\n",
    "            for (pos_s, neg_s) in zip(pos_spec_decay, neg_spec_decay)\n",
    "        ]\n",
    "    )\n",
    "    last_spec_decay_diff.append(\n",
    "        (pos_spec_decay[-1] - neg_spec_decay[-1])\n",
    "        / (pos_spec_decay[-1] + neg_spec_decay[-1] + 0.0001)\n",
    "    )\n",
    "    for i in range(1, len(ratios)):\n",
    "        clip_ratios.append(ratios[i])\n",
    "        clip_diffs.append(pos_diffs[i - 1] - neg_diffs[i - 1])\n",
    "    assert pos_clip_id is not None\n",
    "    assert neg_clip_id is not None\n",
    "    clip_id_to_mean_ear_score[neg_clip_id] = np.mean(neg_scores)\n",
    "    clip_id_to_mean_ear_score[pos_clip_id] = np.mean(pos_scores)\n",
    "    total_clip_ratios.append(\n",
    "        (np.mean(pos_scores) - np.mean(neg_scores)) / (np.mean(pos_scores) + 0.001)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:00.450430Z",
     "iopub.status.busy": "2025-06-01T12:50:00.450283Z",
     "iopub.status.idle": "2025-06-01T12:50:01.244636Z",
     "shell.execute_reply": "2025-06-01T12:50:01.244147Z",
     "shell.execute_reply.started": "2025-06-01T12:50:00.450413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate mean and standard deviation\n",
    "mean_spec_decay_diff = np.mean(spec_decay_diff)\n",
    "std_spec_decay_diff = np.std(spec_decay_diff)\n",
    "mean_last_spec_decay_diff = np.mean(last_spec_decay_diff)\n",
    "std_last_spec_decay_diff = np.std(last_spec_decay_diff)\n",
    "\n",
    "# Plot histogram\n",
    "plt.hist(\n",
    "    spec_decay_diff,\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.7,\n",
    "    label=\"All Spec Decay Diff\",\n",
    ")\n",
    "plt.hist(\n",
    "    last_spec_decay_diff,\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    "    color=\"orange\",\n",
    "    label=\"Last Spec Decay Diff\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_spec_decay_diff,\n",
    "    color=\"r\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"M: {mean_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_spec_decay_diff + std_spec_decay_diff,\n",
    "    color=\"g\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"M + Std: {mean_spec_decay_diff + std_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_spec_decay_diff - std_spec_decay_diff,\n",
    "    color=\"g\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"M - Std: {mean_spec_decay_diff - std_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_last_spec_decay_diff,\n",
    "    color=\"purple\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"Last M: {mean_last_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.legend()\n",
    "plt.title(\n",
    "    f\"Spectrum Decay Difference (pos - neg) (Mean: {mean_spec_decay_diff:.2f}, Std: {std_spec_decay_diff:.2f})\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:01.245559Z",
     "iopub.status.busy": "2025-06-01T12:50:01.245179Z",
     "iopub.status.idle": "2025-06-01T12:50:01.883638Z",
     "shell.execute_reply": "2025-06-01T12:50:01.883143Z",
     "shell.execute_reply.started": "2025-06-01T12:50:01.245543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = clip_ratios\n",
    "y = clip_diffs\n",
    "# Create the 2D histogram (heatmap)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create a 2D histogram\n",
    "bin_edges = np.linspace(-0.5, 0.5, 101)  # 30 bins from -1 to 1\n",
    "hist, x_edges, y_edges = np.histogram2d(\n",
    "    x,\n",
    "    y,\n",
    "    bins=[bin_edges, bin_edges],  # Same bins for both x and y\n",
    "    range=[[-0.5, 0.5], [-0.5, 0.5]],  # Ensure range is from -1 to 1 for both axes\n",
    ")\n",
    "\n",
    "# Create a heatmap using pcolormesh for better control\n",
    "X, Y = np.meshgrid(x_edges[:-1], y_edges[:-1])\n",
    "plt.pcolormesh(X, Y, hist.T, cmap=\"viridis\", shading=\"auto\")\n",
    "\n",
    "# Add a color bar\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Counts\", rotation=270, labelpad=20, fontsize=12)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Clip quality diff ratio difference with prev\", fontsize=12)\n",
    "plt.title(\"2D Histogram (Heatmap) of Correlated Data\", fontsize=14)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"2d_histogram.png\", dpi=300)  # Save to file (optional)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:01.884357Z",
     "iopub.status.busy": "2025-06-01T12:50:01.884203Z",
     "iopub.status.idle": "2025-06-01T12:50:02.872938Z",
     "shell.execute_reply": "2025-06-01T12:50:02.872446Z",
     "shell.execute_reply.started": "2025-06-01T12:50:01.884342Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 ---> -0.1749445865514812\n",
      "0.2 ---> -0.11128298413751571\n",
      "0.5 ---> 0.0006059531352444365\n",
      "0.8 ---> 0.1114644822279522\n",
      "0.9 ---> 0.174413440139695\n",
      "0.1 ---> -0.24679247485450145\n",
      "0.2 ---> -0.14665408033638808\n",
      "0.5 ---> 0.006225104771759251\n",
      "0.8 ---> 0.13863777153500534\n",
      "0.9 ---> 0.20686202231556178\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots side by side\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# First subplot for clip_diffs\n",
    "ax1.hist(clip_diffs, bins=np.linspace(-1, 1, 100))\n",
    "ax1.set_title(\"Differences in quality between positive and negative vs previous chunk\")\n",
    "for percentage in [0.1, 0.2, 0.5, 0.8, 0.9]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(clip_diffs), percentage))\n",
    "\n",
    "# Second subplot for clip_ratios\n",
    "ax2.hist(clip_ratios, bins=np.linspace(-1, 1, 100))\n",
    "ax2.set_title(\"Differences in quality between positive and negative for the same chunk\")\n",
    "for percentage in [0.1, 0.2, 0.5, 0.8, 0.9]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(clip_ratios), percentage))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:02.873641Z",
     "iopub.status.busy": "2025-06-01T12:50:02.873495Z",
     "iopub.status.idle": "2025-06-01T12:50:03.146298Z",
     "shell.execute_reply": "2025-06-01T12:50:03.145815Z",
     "shell.execute_reply.started": "2025-06-01T12:50:02.873626Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_audio_quality_measures(s3_id):\n",
    "    audio_quality = unpacked_pair_quality.get(s3_id, [])\n",
    "    if not audio_quality:\n",
    "        return [None for _ in range(11)]\n",
    "    return [\n",
    "        np.mean(\n",
    "            audio_quality[\"ear_v2_quality_scores\"]\n",
    "        ),  # float(audio_quality[\"ear_v2_quality_scores\"]),\n",
    "        float(audio_quality[\"shimmer_score\"]),\n",
    "        float(audio_quality[\"loudness_factor\"]),\n",
    "        audio_quality[\"spectral_character\"],\n",
    "        float(audio_quality[\"spectral_centroid\"]),\n",
    "        float(audio_quality[\"bass_ratio\"]),\n",
    "        float(audio_quality[\"mid_ratio\"]),\n",
    "        float(audio_quality[\"high_ratio\"]),\n",
    "        float(audio_quality[\"stereo_width\"]),\n",
    "        int(audio_quality[\"total_clips\"]),\n",
    "        float(audio_quality[\"clips_per_second\"]),\n",
    "        float(audio_quality[\"abs_loudness_factor\"]),\n",
    "        float(audio_quality[\"spectrum_decay\"]),\n",
    "    ]\n",
    "\n",
    "\n",
    "df[\n",
    "    [\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    "] = pd.DataFrame(df[\"s3_id\"].apply(get_audio_quality_measures).tolist(), index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.146960Z",
     "iopub.status.busy": "2025-06-01T12:50:03.146815Z",
     "iopub.status.idle": "2025-06-01T12:50:03.230288Z",
     "shell.execute_reply": "2025-06-01T12:50:03.229741Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.146945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34406, 104)\n",
      "(34010, 104)\n",
      "(34010, 104)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df.dropna(\n",
    "    subset=[\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    ")\n",
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.230992Z",
     "iopub.status.busy": "2025-06-01T12:50:03.230843Z",
     "iopub.status.idle": "2025-06-01T12:50:03.250342Z",
     "shell.execute_reply": "2025-06-01T12:50:03.249866Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.230977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 17005\n"
     ]
    }
   ],
   "source": [
    "# Let's use the old selection for now -- for quality assurance\n",
    "# expand the metadata columns -- this takes forever...~ 6 mins\n",
    "# test_slice = df[\"metadata\"].apply(lambda x: ast.literal_eval(str(x)))\n",
    "# test_slice = df[\"metadata\"]  # .apply(lambda x: custom_parse(x))\n",
    "# test_slice_series = test_slice.apply(pd.Series)\n",
    "# df = pd.concat([df, test_slice_series], axis=1, join=\"inner\")\n",
    "print(\"unique_requests\", df[\"request_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.254533Z",
     "iopub.status.busy": "2025-06-01T12:50:03.254244Z",
     "iopub.status.idle": "2025-06-01T12:50:03.340945Z",
     "shell.execute_reply": "2025-06-01T12:50:03.340416Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.254516Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    34010\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    17005\n",
      "True     17005\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-v4-up-u-d-2    34010\n",
      "Name: count, dtype: int64 preference  model_name       \n",
      "False       chirp-v4-up-u-d-2    17005\n",
      "True        chirp-v4-up-u-d-2    17005\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    34010\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row[\"continue_at\"]\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id], row[\"continue_at\"]\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.341620Z",
     "iopub.status.busy": "2025-06-01T12:50:03.341468Z",
     "iopub.status.idle": "2025-06-01T12:50:03.386916Z",
     "shell.execute_reply": "2025-06-01T12:50:03.386445Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.341605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    12364\n",
       "2.0     4641\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=[\"request_id\", \"preference\", \"diff_preference\"])\n",
    "df[\"pos_diff_preference\"] = df[\"diff_preference\"].diff()\n",
    "df[df[\"preference\"]][\"pos_diff_preference\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.387562Z",
     "iopub.status.busy": "2025-06-01T12:50:03.387419Z",
     "iopub.status.idle": "2025-06-01T12:50:03.438697Z",
     "shell.execute_reply": "2025-06-01T12:50:03.438182Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.387547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    34010.000000\n",
      "mean        16.715152\n",
      "std          2.843064\n",
      "min          4.852981\n",
      "25%         14.671549\n",
      "50%         16.721367\n",
      "75%         18.732625\n",
      "max         28.058683\n",
      "Name: mean_ear_score, dtype: float64\n",
      "count    17005.000000\n",
      "mean         0.072990\n",
      "std          2.099652\n",
      "min        -10.275337\n",
      "25%         -1.261453\n",
      "50%          0.105714\n",
      "75%          1.388963\n",
      "max          9.830876\n",
      "Name: mean_ear_score_diff, dtype: float64\n",
      "count    17005.000000\n",
      "mean        -0.003191\n",
      "std          0.129834\n",
      "min         -1.164281\n",
      "25%         -0.077374\n",
      "50%          0.006464\n",
      "75%          0.080768\n",
      "max          0.607376\n",
      "Name: mean_ear_score_diff_ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df[\"mean_ear_score\"] = df[\"s3_id\"].map(clip_id_to_mean_ear_score)\n",
    "print(df[\"mean_ear_score\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff\"] = df[\"mean_ear_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff_ratio\"] = df[\"mean_ear_score\"].diff() / (\n",
    "    df[\"mean_ear_score\"] + 0.1\n",
    ")\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff_ratio\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.439354Z",
     "iopub.status.busy": "2025-06-01T12:50:03.439209Z",
     "iopub.status.idle": "2025-06-01T12:50:03.722160Z",
     "shell.execute_reply": "2025-06-01T12:50:03.721679Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.439339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"pos\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"neg\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:03.722777Z",
     "iopub.status.busy": "2025-06-01T12:50:03.722636Z",
     "iopub.status.idle": "2025-06-01T12:50:04.015748Z",
     "shell.execute_reply": "2025-06-01T12:50:04.015282Z",
     "shell.execute_reply.started": "2025-06-01T12:50:03.722763Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-12.262367089969866\n",
      "-11.915553870089457\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"],\n",
    "    label=\"web\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"],\n",
    "    label=\"mobile\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.legend()\n",
    "print(df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"].mean())\n",
    "print(df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"].mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:04.016406Z",
     "iopub.status.busy": "2025-06-01T12:50:04.016260Z",
     "iopub.status.idle": "2025-06-01T12:50:04.264381Z",
     "shell.execute_reply": "2025-06-01T12:50:04.263911Z",
     "shell.execute_reply.started": "2025-06-01T12:50:04.016390Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"loudness_diff\"] = df[\"loudness_abs\"].diff() / df[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:04.265027Z",
     "iopub.status.busy": "2025-06-01T12:50:04.264884Z",
     "iopub.status.idle": "2025-06-01T12:50:04.512610Z",
     "shell.execute_reply": "2025-06-01T12:50:04.512149Z",
     "shell.execute_reply.started": "2025-06-01T12:50:04.265012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"shimmer_score_diff\"] = df[\"total_shimmer_score\"].diff()\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"shimmer_score_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"shimmer_score_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['shimmer_score_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Shimmer score difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:04.513249Z",
     "iopub.status.busy": "2025-06-01T12:50:04.513105Z",
     "iopub.status.idle": "2025-06-01T12:50:04.875956Z",
     "shell.execute_reply": "2025-06-01T12:50:04.875460Z",
     "shell.execute_reply.started": "2025-06-01T12:50:04.513234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"neg, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:04.876801Z",
     "iopub.status.busy": "2025-06-01T12:50:04.876509Z",
     "iopub.status.idle": "2025-06-01T12:50:04.893069Z",
     "shell.execute_reply": "2025-06-01T12:50:04.892609Z",
     "shell.execute_reply.started": "2025-06-01T12:50:04.876785Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:04.894091Z",
     "iopub.status.busy": "2025-06-01T12:50:04.893750Z",
     "iopub.status.idle": "2025-06-01T12:50:05.146105Z",
     "shell.execute_reply": "2025-06-01T12:50:05.145623Z",
     "shell.execute_reply.started": "2025-06-01T12:50:04.894073Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality diff --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:05.146771Z",
     "iopub.status.busy": "2025-06-01T12:50:05.146627Z",
     "iopub.status.idle": "2025-06-01T12:50:05.174606Z",
     "shell.execute_reply": "2025-06-01T12:50:05.174096Z",
     "shell.execute_reply.started": "2025-06-01T12:50:05.146756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>request_id</th>\n",
       "      <th>pair_quality</th>\n",
       "      <th>preference</th>\n",
       "      <th>loudness_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>c4800cbd-5e1b-4af8-93e2-f1b7f56a25c0</td>\n",
       "      <td>00d8d0b1-9fac-428d-8113-8a3f3127f28f</td>\n",
       "      <td>12.535983</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>16466891-b52e-4ac8-8856-96cef839b258</td>\n",
       "      <td>00d8d0b1-9fac-428d-8113-8a3f3127f28f</td>\n",
       "      <td>18.352567</td>\n",
       "      <td>True</td>\n",
       "      <td>-15.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>f71494c1-86e2-4e4b-b894-da2a1a4d6f0f</td>\n",
       "      <td>0112319e-661f-47b6-8e17-1ab3d0d6a00d</td>\n",
       "      <td>14.944650</td>\n",
       "      <td>False</td>\n",
       "      <td>-13.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>8e514547-cb64-4498-9a02-5223d18e7662</td>\n",
       "      <td>0112319e-661f-47b6-8e17-1ab3d0d6a00d</td>\n",
       "      <td>19.115433</td>\n",
       "      <td>True</td>\n",
       "      <td>-16.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>ec9a3c29-3dfb-4495-9014-7d43dd620578</td>\n",
       "      <td>02814149-8220-4882-beb0-7b4241e1414b</td>\n",
       "      <td>16.869533</td>\n",
       "      <td>False</td>\n",
       "      <td>-14.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>3779f5ca-0d1f-4429-985d-93cbce3bb3eb</td>\n",
       "      <td>02814149-8220-4882-beb0-7b4241e1414b</td>\n",
       "      <td>21.850850</td>\n",
       "      <td>True</td>\n",
       "      <td>-14.074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id                            request_id  pair_quality  preference  loudness_abs\n",
       "178  c4800cbd-5e1b-4af8-93e2-f1b7f56a25c0  00d8d0b1-9fac-428d-8113-8a3f3127f28f     12.535983       False       -23.664\n",
       "179  16466891-b52e-4ac8-8856-96cef839b258  00d8d0b1-9fac-428d-8113-8a3f3127f28f     18.352567        True       -15.171\n",
       "222  f71494c1-86e2-4e4b-b894-da2a1a4d6f0f  0112319e-661f-47b6-8e17-1ab3d0d6a00d     14.944650       False       -13.346\n",
       "223  8e514547-cb64-4498-9a02-5223d18e7662  0112319e-661f-47b6-8e17-1ab3d0d6a00d     19.115433        True       -16.064\n",
       "522  ec9a3c29-3dfb-4495-9014-7d43dd620578  02814149-8220-4882-beb0-7b4241e1414b     16.869533       False       -14.164\n",
       "523  3779f5ca-0d1f-4429-985d-93cbce3bb3eb  02814149-8220-4882-beb0-7b4241e1414b     21.850850        True       -14.074"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_requests = df[(df[\"pair_quality_diff\"] > 0.2) & (df[\"preference\"])][\n",
    "    \"request_id\"\n",
    "].unique()\n",
    "df[df[\"request_id\"].isin(subset_requests)][\n",
    "    [\"id\", \"request_id\", \"pair_quality\", \"preference\", \"loudness_abs\"]\n",
    "].head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:05.175252Z",
     "iopub.status.busy": "2025-06-01T12:50:05.175107Z",
     "iopub.status.idle": "2025-06-01T12:50:05.626933Z",
     "shell.execute_reply": "2025-06-01T12:50:05.626455Z",
     "shell.execute_reply.started": "2025-06-01T12:50:05.175235Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said -1 means stero to mono; 1 means mono to stereo\n",
    "# only cut off the left side\n",
    "df[\"stereo_width_diff\"] = df[\"stereo_width\"].diff()\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"stereo_width_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"stereo_width_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Stereo Width Difference --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:05.627596Z",
     "iopub.status.busy": "2025-06-01T12:50:05.627452Z",
     "iopub.status.idle": "2025-06-01T12:50:06.705984Z",
     "shell.execute_reply": "2025-06-01T12:50:06.705473Z",
     "shell.execute_reply.started": "2025-06-01T12:50:05.627582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\"Spectral Centroid\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:06.706680Z",
     "iopub.status.busy": "2025-06-01T12:50:06.706535Z",
     "iopub.status.idle": "2025-06-01T12:50:07.198241Z",
     "shell.execute_reply": "2025-06-01T12:50:07.197744Z",
     "shell.execute_reply.started": "2025-06-01T12:50:06.706665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AugrK8uXLMWrUKKtVUCZNmoRffvkFAwYMQHBwMPbs2YOhQ4di8+bNeOqpp0p97aVLl+Dl5YUBAwbA19cXSUlJ2L17N3r37o2dO3cWGXhR8HdYr3bt2oq/J3tGFRQb+ueff+Ds7IxXX31VvmWkl52dbaOszJednY1KlSopHnffvn1YsWIFOnfujIULFxr8MR88eDAOHz5sNxPKSZIEtVpd5HyWRBAEo/c1RZUqVfDSSy8ZbHvvvfcwfvx4bNq0CbVq1TL45lo4h5SUFAAo0p+qpO3lYanrxlaUfD+WuDaMlZ2djeTkZJPnairOr7/+ilOnTmH27Nno1asXAOCNN97AmDFjsHLlSvTu3VvujL9z504Auj56Pj4+AHQjGvv164c9e/bIFRRri4qKwo8//ogJEyZg0KBBAHQziL/wwgtYsGABduzYUerri1uOpHfv3mjTpg22bdtWpGWkuN/hiqZC90GxtbS0NPj5+RX7IVT4A27ZsmX4888/cenSJWzZsgUrV67Ezp07i20GzMzMxP79+7FhwwasXLkSX375Jc6fP19kP41GI982WblyJT7//HP89NNPSEtLQ3p6OtavXw8AOHHiBJYtW4Zly5bJfSX279+PL774ApmZmXjvvffQpEkTvPfeewB0Fa8xY8bgueeeQ8OGDdGmTRvMmTMHubm5Zh2nJUuWwMfHB3PmzCm2paF169Zo27at/Dg/Px9Lly5Fx44d5fLnz5+P/Px8g9eFhYVhxowZ2L9/P1544QU0bNgQ3bp1w6FDh+R9li1bhvnz5wMA2rdvLy+iGBcXZxBj79696NatGxo1aoTDhw8DAM6fP4/BgwejadOmaNKkCQYOHFhktt+S+qDs3LkTHTp0QHh4OHr16oV//vnHrGNXkKurK+bPnw8fHx+sXr3aYMh7wb4OkyZNkufnGTt2LMLCwuT+LxMnTgQA9OrVC2FhYZg0aZIc48yZMxg0aBCefPJJREREoF+/fvj3338NctD3O7p69SrGjx+Pp59+2qCi9N1336Fnz54IDw9Hs2bN8M477+DOnTsGMfr3748XXngBV69eRf/+/REREYHWrVtj3bp1Rd5zXl4eli1bhs6dO6NRo0aIjIzEqFGjDDrHS5KETZs2yeevZcuWmDJlCtLS0so8pvq+Drdu3cKQIUNM/j2YNGkSvvzyS/kc6H+KOy96xlxXSkhJSUHHjh0xYMAAfP/99+Vaf0x/HXTr1s1ge9euXZGXl4cDBw7I2zIzM+Hi4lKktTIwMBCurq4AdAMa9Et9LF++XD5uhY9VQkIC3n77bTRp0gTPPPMMPvnkE7NHXO7btw+iKOK1116Tt7m4uKBXr144depUkevUGPpBFvr+goXl5+c75JdVpVALig15enri7t27SE5ONmoo7+3bt3HlyhWEh4dDFEVER0dj7969ePXVV+XXZ2dn46uvvgJjDI0aNYKbmxtu3ryJAwcOID8/Xx7pI0kSfvjhB8TGxiI0NBQRERFQq9W4deuW/K3pueeewx9//IGQkBB5HpSAgAA5H0mS8Ndff8HX1xcTJ06UPzz27duH3NxcvP766/Dx8UFUVBS2bt2Ku3fvGnRANkZMTAyuX7+OV155pcx5W/Q5jRgxAv/++y9effVVhISE4PLly9i8eTNiYmKwcuVKg/3//fdf/Prrr3jjjTfg7u6OLVu2YMyYMfj999/h6+uLjh07IiYmBj/88AM++OADeXi2fgZhADh27Bh+/vln9O3bF76+vqhevTquXLmCvn37wt3dHYMHD4ZKpcLOnTvRv39/bN26tdTbd1999RWmTJki//GJjY3FiBEj4O3tjapVq5p0/Apzd3dHhw4d8PXXX+Pq1auoV69ekX1ee+01VKlSBatXr0b//v3RqFEj+bzXrl0bO3fuxJgxYxAUFISaNWsCAI4ePYohQ4agYcOGGDVqFBhj+OabbzBw4EBs27YN4eHhBmWMHTsWtWrVwjvvvCNXlFatWoUlS5bg+eefR69evZCSkoKtW7eib9+++Pbbbw3+YKWlpWHw4MHo2LEjnn/+efzyyy9YsGABQkND5fW1tFothg0bhqNHj6Jbt24YMGAAsrKy8Ndff+Hy5cty7lOmTMGePXvQs2dP9O/fH3FxcXKlfvv27WXeftNoNHLFzNTfg9deew337t3DX3/9JVeES1Oe68pUlStXxsSJE/HNN9/gvffeg5eXF1588UX06tULTzzxhEmx8vPzIYpikWOp778UHR0tz7DdrFkz/PTTT5gyZQreeust+RbPb7/9hvfffx+A7vdv2rRpmDZtGjp27IiOHTsCgEHlTn9bKTw8HBMmTMDRo0fx+eefo0aNGsX2eynLhQsXEBwcXORzSH9tX7hwwajfz/T0dGg0GiQmJmLz5s3IzMyUK1sFHTt2DI0bN4ZWq0X16tUxcOBADBw40OS8HRonFtGvXz8+a9asUve5efMmX758OV++fDn/6quv+JEjR/jNmze5RqMpsu/SpUv50qVLeUJCgrwtPT2dr1y5kv/444/ytv379/MNGzbw7Oxsg9fv27ePr1mzhqvVas455+fOneNLly7lp06dKlKWJEmcc86zs7P50qVL+bFjx4rs89tvv/H169fzs2fPFnkuJyenyLY1a9bwsLAwfvv2bYP3FBoaWmTfgvbv389DQ0P5xo0bS91P79tvv+X169fnJ0+eNNi+fft2Hhoayv/99195W2hoKG/QoAG/efOmvO3ChQs8NDSUb9myRd62fv16HhoaymNjY4uUFxoayuvXr8+vXLlisP3tt9/mDRo04Ldu3ZK3JSQk8CZNmvC+ffvK244dO8ZDQ0PlY5yfn89btGjBX3rpJZ6Xlyfvt3PnTh4aGsr79etX5jFo27YtHzp0aInPb9y4kYeGhvL9+/cbvI+lS5cWyevnn382eO3u3bt5aGgoj4qKkrdJksQ7derE//e//8nXDue666Bdu3b8rbfekrfpz/m7775rEDcuLo4//vjjfNWqVQbbL126xJ944gmD7f369eOhoaF8z5498ra8vDzeqlUrPnr0aHnb119/XeK1o8/z5MmTPDQ0lO/du9fg+UOHDhW7vbCJEyfy0NBQvmDBgiLPGft7MH369BJ/DwqfF2OvK6WdOXOGT5kyhT/11FM8NDSU9+jRg2/dupWnpaUZ9frPP/+ch4aGFvm9XLBgAQ8NDeXDhg2Tt2k0Gj5jxgzeoEEDHhoaykNDQ/njjz/Ot23bZvDa5OTkIsdHT39eli9fbrC9R48e/OWXXzb2bRvo1q0bHzBgQJHtV65c4aGhoXz79u1GxencubP8vho3bsw/++wzrtVqDfYZNmwYX7t2Lf/tt9/4V199xd944w0eGhrK58+fb1bujqpC3+JZs2YNXnnlFTRp0gQtWrTA22+/jevXrxvsk5eXh+nTp6N58+Zo0qQJRo8eLa/DAzxsojdnqGXNmjXRq1cv1K5dG0lJSfjvv//w3XffYePGjUXyAICqVauicuXKSE1Nxfjx49GmTRusWbMG69evR0ZGBjjnuHbtmtyRKicnR/65cOECtm/fjqeffhphYWGIjo6Gm5ubwTfbdu3aISwsDPXr10dYWBgaN25cpIm+sOJmmNV/gwR0LTopKSlo0qQJOOfF3moqTWZmJgDdN39j7Nu3T27xSUlJkX+eeeYZAChyK6Vly5byN2lAN8W/h4eHScO/n376adStW1d+rNVq8ddff6FDhw4G9+8rV66MF154Af/++6/8vgqLjo5GcnIy+vTpY7C8wMsvv6xYvw/9sczKylIk3oULFxATE4MXX3wR9+/fl495dnY2WrRogZMnT0KSJIPX9OnTx+Dxb7/9BkmS8Pzzzxuct4CAANSqVavIeatUqZLB/XlnZ2c0atTI4Lz9+uuv8PX1LXY5CcYYAN314unpiVatWhmUq++0bszwbwB4/fXXi2xT8vcAKN91VV7h4eGYPn06jhw5ggULFsDb2xszZ85EZGQk3nvvPcTHx5f6+hdeeAGenp748MMP8ddffyEuLg47d+7Etm3bAMDgtpcoiqhRowYiIyPxySef4LPPPkPbtm0xa9Ys7N+/36S8C5+XJ598Ur49a6rc3Nxil/zQ36I39hb23LlzsX79ekydOhUhISHIy8srcttp9erVGDJkCDp06IBevXph69atiIyMxKZNm3D37l2z8ndEFfoWz4kTJ9C3b180atQIWq0WixYtwqBBg/Djjz/KfUDmzJmDP//8E4sXL4anpydmzpyJUaNGldkhylhVqlRB165dodVqkZSUhOvXr+P06dPYt28f+vTpY3ArQT9h2nvvvYfExERs3LgRZ86cwfLly/HRRx9h7ty5yMvLQ3R0NKKjow3KOXv2LGrVqoVOnTph06ZNSE9Ph4+PDwTBsI46ZswYuak1NzdXXq25OIIgFDvEND4+HkuXLsXBgweL3Mc39QNU35xq7B/Tmzdv4tq1a8U2mQK6JQEKKq5J1tvb26QKZ+ERTikpKcjJySm2x31ISAgkScKdO3eKvb2i/6CvVauWwXYnJydFOisCD4+lsZW+ssTExACA3D+lOBkZGQYT/hU+ZjExMeCclzhcvPCyEY899phcydDz9vY2GE5/69Yt1K5du8hrC7p58yYyMjKMvl5Kyu2xxx4rsl3J3wOgfNeV/vUF/xBWqlQJ7u7uRRYZ9fT0NKhcFeTi4oIXX3wRzz//PLZv345PPvkE33//Pbp06YJq1aqVmHtgYCBWrVqFCRMm4H//+x8A3e/2xx9/jIkTJxr0uVu7di2++OIL/PLLL/I12rVrV/Tv3x/Tp0/Hc889V+o5LZhrwc9PQHeNGNO3qDiurq5F+rEBkPvmlHTMCis4P0u3bt3QtWtXAKX//jDG8Oabb+LIkSM4fvx4hek8W6ErKBs2bDB4PG/ePLRo0QLnzp3D008/jYyMDOzevRsLFiyQP8DmzJmDrl274vTp0wgICMCAAQMA6L5FA7pvuvo5UDjnmD9/Pr7++ms4OTmhT58+Jc51IIoiqlSpgipVqsDHxwf79+/H1atX0axZM4P9rl27hsOHD+Prr79Go0aNkJOTg2effRY//PAD3nnnHQC6+7CPP/64wev0k9HduHEDmzZtKvGYuLu7y2v65OTklHr/XRTFIn8ktFot3nrrLbmPQJ06dVCpUiUkJCRg0qRJRb5Jl0XfQlNwkcXSSJKE0NDQEldgLvyHpKQhnNyENZOM/WCyF1euXAFQtBJkLv2xmjBhQpHrTq9wp+/CHcMlSQJjDOvWrSv2nBR+vVJDbyVJgr+/PxYsWFDs84X/wBXH2dm5SEVf6d8DJfTq1cugU/2oUaMwevRoREZGGuw3d+5c9OzZs9gY165dw+7du7F3714kJiaiXr166NWrl1HDfJ9++mns378fly9fRnZ2NurXr4979+4BAIKDg+X9tm3bhubNmxepQLdv3x5z587F7du3jbp2lR6eHRgYiISEhCLb9RW8guu1Gcvb2xvPPPMMvv/++1IrKMDDL1PmVrAcUYWuoBSm70mt/6YXHR0NtVptMN9DSEgIqlWrhtOnT6N///5YtmwZRo8ejX379sHDw8Pgj9WePXvw1ltvYdeuXTh9+jQmTZqEpk2bypP6TJo0Cbdv38aWLVsM8tBf6IVbDdLS0nDq1Cl4eXmhUaNGAIDU1FTUqVMHgiDg0qVLcHZ2Bue8xG/b+m/oXl5eSE1NhVarNfhFXrduHVatWoWqVauic+fOJn+QXr58GTExMfjkk0/kShEA/PXXXybF0atduzZq166NAwcOICsrq8xv/TVr1sTFixfRokWLIpUnc5kax8/PD25ubrhx40aR565fvw5BEErsTKf/Fnrz5k2Db/VqtRpxcXFGLVJZmqysLOzfvx9Vq1ZFSEhIuWLp6a81Dw8Ps+dGqVmzJjjnCAoKUmyuh5o1a+LMmTNQq9UlVrRr1qyJo0ePomnTpopWNE35PTD2+irPdQUAn376qcFIHP1527hxo8F+BW9XArrPxZ9++gm7d+/GmTNnUKlSJXTt2hW9e/cudf2v4oiiaFCJ/fvvvwHA4LpJSkoq9nNHrVYDgDylgFK/38aqX78+jh8/jszMTIOOsmfOnAGAEivnZcnNzS1xFE9B+tuXxlSaHxUVug9KQZIkYc6cOWjatKk8q19SUhKcnJyKDHfz9/dHYmIiRFGUKzP+/v4IDAw06CcQFhaGUaNGITg4GD169EDDhg0NFvlzdXUt9gNF32ReeEG/O3fuICYmRr5AMzIycOPGDdSqVQve3t5ITk5GSEgIrl27VmzTdE5Ojvz/2rVrIycnB2fPnpW39e/fH4sWLcLmzZvx2muv4fPPP8dff/1l0vBC/TfJgi0QnHN88cUXRscobMyYMUhNTcVHH31U7HwnR44cwe+//w4AeP7555GQkFBkVlxA90FgzpA9/W0sYz5EAN2HcKtWrXDgwAGD+91JSUn44Ycf8OSTT5Y4Iqlhw4bw8/PDjh07DJqT9+zZU+4p5XNzczFhwgSkpqZi+PDhin3AN2zYEDVr1sTnn39e7K04/dwppenUqRNEUcTy5cuLtF5xznH//n2T8+rUqRPu378vD+MtHBPQXS9arbbI6C5A94fQ3GNuyu+B/voqq6zyXFeArv9Fy5Yt5R99BaXgtpYtW8pfkPRTCERGRmLKlClgjGHWrFk4cuQIZs+ebXLlpLCUlBSsX78eYWFhBhWU2rVr4++//zY451qtFj///DPc3d3lPmPGHjeldOnSBVqtVp6nBdCNTvrmm28QERFh8FkeHx+Pa9euGby+uM/kuLg4HD16FA0bNpS36b84FqRWq7F27Vo4OTlZbVI6e0AtKA9Mnz4dV65ckTttKaHwOjOBgYEGF2loaCjUajWOHDkCX19faLVa3LlzB1evXoWXlxe+/vpreSZBtVqNyZMn4+DBg8jNzcW///4r9zMpeMG2bNkSt2/fxldffYUnnngCfn5+yMvLw7179xAXFyd3iq1Xrx5iY2Nx+PBhJCQkoGrVqoiIiEBsbCwCAwPx+uuvw8nJCR9//DEuXrwIHx8fuLq6wt/fv9Qh0XXq1EHNmjXxySefICEhAR4eHvjll1/K9SHStWtXXLp0CatXr8b58+fxwgsvyDPJHj58GEePHsXChQsBAC+99BJ+/vlnTJ06FcePH0fTpk2h1WrlmVTXr18vtz4Zq0GDBgCAzz77DF27doWTkxPatm1b6mRc48aNw99//4033ngDb7zxBkRRxM6dO5Gfny8PlSyOk5MTxo0bhylTpmDgwIHo2rUr4uLi8M0335jUByUhIQHfffcdAF0HzWvXrmHfvn1ITEzE//73vyKdVMtDEATMmjULQ4YMwQsvvICePXuiSpUqSEhIwPHjx+Hh4YHVq1eXGqNmzZoYN24cFi5ciNu3b6NDhw5wd3dHXFwc9u/fj1dffVWeHMtYPXr0wLfffou5c+ciKioKTz75JHJycnD06FG8/vrr6NChA5o1a4bXXnsNa9aswYULF9CqVSs4OTkhJiYG+/btw4cffoguXbqYfExM+T3QX1+zZs1CZGQkRFEsMl+InrnXlTlSU1Nx5MgR9OnTB7169Sqxb4ux+vXrh8aNG6NWrVpITEzErl27kJ2djdWrVxvcIhsyZAjef/99vPrqq3j11Vfh6uqKH3/8EefOncO4cePk1jBXV1fUrVsXP//8M4KDg+Hj44N69eoVmTa+LJMmTcKePXtw4MCBUmfMjoiIQJcuXbBo0SIkJyejVq1a2LNnD27fvo3Zs2cb7Dtx4kScOHHCoE/Uiy++iBYtWqB+/frw9vZGTEwMdu/eDY1Gg/Hjx8v7HTx4EKtWrULnzp0RFBSEtLQ0/PDDD7h8+TLeffdd+RZ8RUAVFAAzZszAH3/8ga1btxr0UQgICIBarUZ6erpBK0pycrJRF0nhjlyMMYNvVK1atcLVq1cRExOD6OhoSJIET09PNGrUCE899RRycnIwZMgQAMAXX3yB6tWro3Hjxjh58iSOHz8OPz8/tG/fHj4+PkhLS0NgYCAqVaqE3r174+TJk7h+/Tqio6Pl9X1atmwpd84TBAHdu3fHyZMncfnyZVy7dk1u0dFXQCIiIuSZUY8cOQKtVotmzZqVWkFxcnLC6tWrMWvWLKxZswYuLi7o2LEj+vbtW66OXe+88w6eeeYZbNmyBdu3b0daWhq8vLwQERGBlStXon379vL7WrFiBTZt2oTvvvsOv/32G9zc3BAUFIT+/fubdfsgPDwcY8eOxY4dO3D48GFIkoQDBw6UWkGpV68evvzySyxcuBBr1qwB5xzh4eH49NNPy5yr4rXXXoNWq8WGDRswf/58hIaGynOEGOvChQuYMGECGGNwd3dH1apV0bZtW/Tu3bvInCRKaN68OXbu3ImVK1di69atyM7ORmBgIMLDww0mtirN0KFDERwcjE2bNmHFihUAdH2GWrVqhXbt2pmckyiK8i3LH374Ab/++it8fHzQtGlTgy8PM2bMQMOGDbFjxw589tlnEEUR1atXR/fu3dG0aVOTywVM+z3o1KkT+vfvjx9//BF79+4F57zECkp5ritTValSBYcOHSp25Io5GjRogH379skVtpYtW2LcuHFFKt7du3eHr68v1q5diw0bNiAzMxO1a9fG9OnTi1SsZ82ahZkzZ2Lu3LlQq9XFrmtTFv0aaMYsYzF//nwsXrwYe/fuRVpaGsLCwrB69Wq5D2JpXn/9dfzxxx84fPgwsrKy4Ofnh1atWmHYsGEG12NoaChCQkKwd+9epKSkwMnJCY8//jgWL16M559/3qT35vCsO6rZvkiSxKdPn84jIyP5jRs3ijyfnp7OGzRowPft2ydvu3btGg8NDZXnD/n33395aGgoT0lJMXhtcfOgjBgxgk+cONGsXJcuXcr/+OMPfvXqVR4aGmow/8jhw4d5WFgYv3v3bplx9PNbGDN/wXfffcfr16/PU1NTzcqZEELsXYsWLfi8efNsnQYpRoXugzJ9+nTs3bsXCxculIfbJSYmyuPZPT098corr2DevHk4duwYoqOjMXnyZDRp0kS+/1q9enUwxvDHH38gJSXFpLklFi5ciAkTJpiUc0hICFq3bo2PP/4YUVFR+PfffzFz5kx069YNVapUAaBr3u/SpQuioqLk1yUmJuLChQvyFN+XL1/GhQsXkJqaCkC3IN6mTZtw8eJFxMbGYu/evZg7dy66d+9uMDyUEEIeFVeuXEFubq7cUk3sS4W+xbN9+3YAus6hBRUcZjd58mQIgoAxY8YgPz8fkZGRmDp1qrxvlSpVMHr0aCxcuBAffPABevToIQ8zLktiYqJZ6zcsWLAAM2fOxMCBAyEIAjp16mSwgJZarcaNGzcMOsXu2LEDy5cvlx/37dvX4L06Ozvjp59+wvLly5Gfn4+goCC8+eabRVbTJISQR0W9evXw33//2ToNUgLGuQkTPhCbWbZsGcLDw+V1RgghhJBHGVVQCCGEEGJ3KnQfFEIIIYTYJ6qgEEIIIcTuUAWFEEIIIXaHKiiEEEIIsTsOPcw4OTkDZXXx1c2k6WL11UMHD/4fwsLC8P77pa9Q6egEQUBWVp5Jq/8+6lhiIly+24O8l14Gt8C01JaMb+ncHSEPJco2N4YtyybEGhgD/P09y94RDj6KJymp7AqKIDysoBTed/XqlVi71nCNkODgYHzzzV75cV5eHhYtWoBff92H/Px8tGjREh988JE83fs//5zE0KGD8OefR+Dp+XCq5CFD/ofQ0PJXUMoqvzicc6xevRJ79uxGRkYGIiIaY/Lkj1Cz5sMlytevX4sjRw7j8uVLUKmccOiQ6asNM/awgiJJDnsZEUIIsRLGgIAA4yooFf4WT0hICH799aD8s2HDZoPnFy6cj8OH/8QnnyzAunUbkZiYiPfee8dq+ZlT/ubNG7F9+zZMnvwxNm/+Em5ubhg5crjBqsRqtRodOnRCr16vWvotVDgs9T6c9+4BSzV9BV5bx7d07o6QhxJlmxvDlmUTYm8qfAVFFFUICAiQf3x9feXnMjIy8O23e/Duu++hWbPmeOKJJzBt2kycOXMaUVFnEB9/G0OH6lZYbdMmEk2bhmPq1IczunLOsXjxIjz3XCQ6dmyL1auLLulemrLKLw7nHNu2bcXgwUPw3HNtERoaihkzZiMxMRF//HFQ3m/EiJHo168/6tYt3wqlpCjx1k14Dx4I8dZNh4tv6dwdIQ8lyjY3hi3LJsTeVPgKyq1bN9GpU3u8+OLz+PDDSQZTz1+4cB4ajQbNmz8jb6tduzYee6wqoqKiUKXKY/j000UAgD179uLXXw/ivfce3tL54Ye9cHNzwxdffImxY9/BunVrcOzYUfn5qVM/wpAh/ysxt7LKL87t27eRlJRk8BpPT080bNioxEoNIYQQYm8cupNseTVq1AjTp89CrVrBSEpKxNq1qzFo0Jv46qtv4O7ujuTkJDg5ORn0LQEAf39/JCcnQRRFeSE9Pz+/IvvVrVsPw4aNAADUrFkLO3fuwIkTx/HMMy0AAAEBgaV23i2r/JJeo8vHsI+Kv78/kpKSyzokhBAHwzmHJGnlzxJJYMitVQv5AoNWnW/j7EhFIwgCBEEEY6zcsSp0BaVVq9by/0NDQ9GoUSN069YFv/32C3r06Fnu+PXqhRo8DggIQEpKivx49Oix5S6DEFJxaTRqpKWlQK3OfbjRqxKSVq6C5FUJSDZ9MVJCysvZ2RVeXn5QqZzKFadCV1AK8/T0Qs2atRAbGwsA8PcPgFqtRkZGukErRnJyMvz9A8qMp1IZHl7GGDg3frizOeXrt6ekJCOwwBDD5ORkhIWFGV02MR93dYO6UQS4q5vDxbd07o6QhxJlmxvDlNdxzpGcfBeCIMDbOwCiqNJ9a83Lg5CrheT3GODiYu5bIMRknHNotRpkZqYiOfkuKlcOKldLClVQCsjOzkZcXCy6dXsBAPD4409ApVLhxInjaN++IwAgJuYG7t69g/DwcACAk5OuhqjVKj/PijHlF1a9enUEBATgxInjCAurDwDIzMxEdPRZ9O5NI3asQRsahtQDhx0yvqVzd4Q8lCjb3BimvE6jUYNzCd7egXB2dn34hJMzEFofosmlE6IEF4iiiJSUBGg0ajg5OZsdqUJXUD77bAGeffY5VK1aFYmJiVi9eiUEQUSXLs8D0HUu7dHjZSxcuABeXt5wd/fA/PlzER4egfDwCABA1apVwRjD4cN/IjKyNVxcXFGpUiWjyl+2bAnu3UvAzJlzin3emPIBoGfP7hg1aizatWsPxhjeeKMf1q9fi5o1a6JatepYtWoFAgMD8dxz7eTX3LlzB+npabh79w4kSYtLly4CAGrUqGl0/oQQ22Oswo91IHZGqWuyQl/ZCQn38MEHE/Hyy90xceJ78Pb2webNW+Hr6yfvM378BLRu/Szef/9dDB78Jvz9A7BgwWfy85UrV8Hw4W9j2bIl6NChLT75pPjKRnGSkhJx9+7dUvcpq3wAiImJQWZmpvx44MC30KfPG5g1awb6938D2dnZWL58FVwKNPeuXr0Cr7/+KlavXons7Gy8/vqreP31V3H+/Dmj8yfFU509g4CgAKjOWmbUlCXjWzp3R8hDibLNjaHI+87OhurMaSA72/wYhNiBCj2TLCkfmkm2eKqo0/Dt8Czu7z8ETXhjh4pv6dwdIQ8lyjY3himvU6vzkZx8B/7+VQ2a0VlODlQ3rkEbUhdws3wfHs45fbYSAyVdm4BpM8lW6Fs8hBDyKGEMEDxcoK5VDVIlJ6t8wjsxEVKeliopduDu3btYuHAu/vvvH7i5VcLzz7+AYcNGFhmwUVB6eho+++xT/PXXYQgCQ5s27TB27Hvyrf68vDwsWDAXly5dwM2bMWjZMhJz5y60yvuhCgohhDwiGGNQgyMmLRl52nzAqXzDPMviLIio7eULFWO0YKiNabVaTJgwFn5+/li9+nMkJSVh9uypUKlUGDZsZImvmz79YyQnJ+Gzz1ZAo9Fg7tzpmD9/NqZNmw0AkCQJLi4u6NWrj8Fs5NZAFRRCCHnEqNVq5Gu1gGB/3QxHjRqKOnVCAAC//PITVCoVevTohcGDh8tDUtPT07FkyQL89ddhqNX5aNz4SYwb9x5q1KgJALh79w4WLZqPqKjT0GjUeOyxahg5cgxatIg0KocNG9bg8OE/0avXa/j887XIyEhH587d8M4772PHjq3YuXMbJElC7959MHDgIPl1GRkZWLFiMY4c+RP5+WrUr/84Ro9+V57z6vbtOCxbtgjnzkUjNzcHtWrVxrBhI/H0083lGL16vYju3V9GXFwsfv/9ADw9PTFw4CC89FL55t46ceIYYmJuYPHilfDz80e9emEYPHg4Vq1ahv/9b6g84rSgmJgbOH78b6xf/wXq138CADBu3Pt4//2xGDVqHAICAuHm5ob33vsAAHD27BlkZmaUK09T2N/VS4iD09QLQ8qh49DUs8y8M5aMb+ncjaUNDcP9w8rkwZjux1hKHANzYyhy/EURkq8fUEqzvq39/POPEEUV1q3bjLFj38POnV/i+++/lZ+fM2caLl26gE8+WYTVqzeCc4733x8LjUYDAFi06BOo1flYsWIdNm/egREjRsPNzbTRh7dvx+HYsb+xcOEyTJ06Gz/++B3ef38cEhPvYfnyNRgxYjTWrVuFc+ei5dd8/PFE3L+fggULlmLDhi0IDa2PceNGID09DYBuqopnnmmFJUtW4vPPv0Tz5i0wceK7RQZD7NjxJerXfwIbN36Jl1/ujYUL5+HWrRj5+VGjhmL27GkmvZ9z586iTp26BrOIN2vWAllZWbhx41qxr4mOjoKHh6dcOQGAp55qBkEQDN63rdjvFezgpk79CBkZGVi0aImtUyHW5uYGbf3HHTO+pXM3AmOA4OMB7tMQrJx9GxgDBBfdjCBG95NQ4hiYG0OJshnT3drRassXx4KqVKmCMWPeBWMMNWsG49q1q9i1axu6d38ZsbG3cOTIIaxatQGNGummU5g6dSZ69uyGQ4f+QLt2HZCQcBdt2rRDSEhdAED16kEm58C5hMmTp6BSJXfUrl0HTZo8hdjYm1iwYAkEQUDNmsH48svN+O+/f9CgQUOcOXMaFy6cw/ff/wZnZ13Hz1GjxuHw4T/w++8H8NJLPVGvXqjBDOJDhozAoUO/46+//sQrr7wmb2/RoiV69uwNAOjXbyB27dqG//77BzVrBj84Po8ZNRloQcnJyfDz8zPYpq+sJCcXv8xJSkqywQK5gG6CUU9PL6Sk2H5plArdgqLVarFy5XK88EIXtGjxNLp374p169YY3EvlnGPVqhXo1KkdWrR4GsOHD8GtAquExsffRtOm4fI8Ikorq/zi/PvvPxg7dhQ6dWqPpk3D8fvvpd83nD17Jpo2DceXX25RMvUKS4i9BY93RkGIveVw8S2duzEYY9DejIHbyBEQ42LLHUvNJai5ZPSMlkocA3NjKHL8JS1YRjqg1Zgfw8KeeKKhwflo2LARYmNvQavV4ubNGxBFEU880VB+3tvbBzVr1sLNmzcAAL169cHmzRswYsT/sGHDGly9esXkHB57rBoqVXKXH/v5+SE4uDaEArfF/Pz8kZqqW57k6tXLyMnJQbdu7dGxY2v5586deNy+HQcAD6Z0WIy+fXuhS5fn0LFja9y8GYOEBMMWlJCQh6vIM8bg5+eP+/fvy9s+/ngGhg8fVWLu48ePkcvv1+/RnYCzQldQNm36HF9/vQsTJ07G7t3fYsyYcdi8eSN27Ngm77N580Zs374Nkyd/jM2bv4SbmxtGjhyOvLw8q+RoTvm5uTkIDQ3DpEmTy4x/8OABnD0bhcDAykqmXaEJ91Pg9uUXEO6nlL2zncW3dO5G55GSArctX4ClWD8PJY6BuTEUOf4SB8vJAR7hof8vvtgDu3Z9h86du+LatasYPLg/vv56h0kxiluKpLjRLvopFHJysuHvH4CNG7cZ/GzbthtvvDEAALBixWIcOvQ7hg4diRUr1mPjxm2oU6cu1GrDymJxZZe2cGxhkyZ9JJe/YIGuld7f399grTcAciuIv79/kRgAilSMAECj0SAjI73IgrO2UKErKGfOnEGbNm3RuvWzqFatOjp06IRnnmmB6GjdvTfOObZt24rBg4fguefaIjQ0FDNmzEZiYqLcm/mFF3Szzr7++qto2jQcQ4b8z6CML77YhE6d2qFt29aYO3c21Gq10fkZU35xWrVqjZEjR6Ndu/alxr93LwHz58/F7NlzSx2GRgghSio8IeS5c9GoUaMmRFFErVq1odVqcf78wz4QaWmpuHXrJoKDa8vbqlR5DD169MKcOZ+iT59+Bn1YLCEsrD5SUpIhiiKCgmoY/Pj4+ADQdSLt2vVFtGnTFiEhuv4gd+/GK55LYGBluezHHqsKAGjQoBGuX7+K+wUqtydPHoe7uzuCg+sUG6dhw3BkZmbg4sUL8rb//vsHkiShQYOGxb7Gmip0BSUiIgInThzHzZsxAIDLly/h9OlTaNVK1xP89u3bSEpKQvPmz8iv8fT0RMOGjRAVpZvpccsWXWvLqlVr8euvBw1mef3nn5OIi4vFmjUbMH36LHz//Xf4/vvv5OdXr16Jbt26lJifMeWbS5IkfPTRZAwY8KZ8H5cQQqwhIeEuli1bhFu3YvDbb/uwe/dO9OrVB4BuuY3Wrdvgk09m48yZ07hy5TJmzJiCwMDKaN36OQDAkiULcfz4UcTH38alSxfx33//oFat2qWUWH5PPdUcDRo0wgcfvIcTJ47hzp14nD17BmvWrMDFi+cBAEFBNfHnnwdx5colXLlyGdOnf2jWJJYzZ07B6tXLTXpNs2bPIDi4NmbOnIIrVy7j+PGjWLduFXr2fFXuM3P+fDTeeOMVJCbeAwAEB9dG8+YtMX/+LJw/H42oqNNYtGg+2rfvhICAh4vN3rhxHVeuXEJ6ehoyMzMfvL9LJr8vU1Xor81vvTUIWVlZ6NnzJYiiCK1Wi5EjR6Nr124AgOTkJAAo0tTl7++PpCRd05m+g5GPjw8CAgw7NXl6emHixMkQRRG1a9dG69bP4sSJE+jZs9eD1/giKKjkzl3GlG+uTZs+h0qlwuuv9y1XHEKI/XFycoIkioBo2SUDnQXz4nfp0g15eXkYMmQgBEFEr159DIbZfvDBVCxZsgATJ46DWq1GRERTfPrpErmlV5K0WLToEyQm3kOlSu5o3rwFxox5V359r14v4vnnX8CgQcPK9wYLYIxhwYIlWLt2JebMmY7U1Pvw8/NH48ZN5eVRRo9+B3PnzsDw4f+Dt7cP+vYdiKysLJPLSki4a9AXxhiiKGL+/MVYsGAuhg9/C25ubujSxfAY5Obm4tatm/JoKEDXAXnRovkYO/ZteaK2cePeN4j9/vtjcffuHfnxW2/p/m4cOfKPye/NFBW6gvLbb7/g559/xJw581CnTgguXbqEhQvnIzAwEC+++FK544eEhEAs8AEREBCAK1cedubq0+d19OnzernLMdX58+exffuX2LZtZ7mWwibFkwIrI3vMu5As1K/HkvEtnbvReVSujKx33gWvbP08lDgG5sYob9mccziJImpXCQJ3dQHMrECYwokJkLhpI4ZUKhXGjh0vz69RmJeXFz7+eEaJr3/nnQklPpebm4uUlBQ0afJkifsMGjSsSOXlww+nFdlv+fK1Bo8rVXLHuHHvF/kDrle1ajUsXbraYNsrrxh2Yv366++LvG7Tpm0GjwuXa6zHHquKBQuWlvh806ZPFalUeHl5y5OylaS4nK2hQldQFi9ehDffHITOnXX9SOrVC8Xdu3ewceMGvPjiS/Iwr5SUZAQGPmzuSk5ORlhY2fMUFO3XYdpsi+UtvySnTv2LlJQUdO3aWd6m1Wrx2WcLsW3bl/jxx31mxyaAVLUasj6a5pDxLZ270XlUq4bMadOhUsPqnT2VOAbmxihv2ZwDkgYQnSoBWuh+LEzi9jXN/X///YMnn3wKTZs+ZetUSDlV6ApKbm4uBMGwBUEQBPmeYfXq1REQEIATJ44jLKw+ACAzMxPR0WfRu7euVqyfnU+rNb4HtrGMKd8c3bq9aNCvBQBGjhyBbt1eQPfu5W85quhYZgZUZ05DE9EY3MO4RbHsJb6lczc6j4wMqE6fBm/QGHD3kCdas8YfQiWOgbkxlCiba7RATja4WyWL3+KxRy1bRqJlS+NmlCX2rUJ3kn322TbYsGEdDh8+hPj42zh48AC2bt2Ctm3bAdDdc3zjjX5Yv34t/vzzd1y5chlTpnyIwMBAPPecbh9fXz+4urri77+PIDk5GRkZxk8DvGPHdgwbNrjE540pHwCGDRuMHTu2y4+zs7Nx6dJFeW6W27d1Hcnu3NHdQ/Tx8UHduvUMflQqFfz9/Q16yRPziNevweflbhCvFz97oz3Ht3TuRudx7Rr8XugK8fo1ebI1wUU0aUZYs8tW4BiYG0OR45+XB/HqFcBKUyGYavnytRg7dryt0yAOoEK3oEyY8AFWrlyOuXNn4/79FAQGBuKVV3ph6NDh8j4DB76FnJwczJo1AxkZGWjcuAmWL18FFxcXALrbOO+/PxHr1q3B6tUr0aRJU6xb97lR5aem3kdcXFyp+5RVPgDExcUhNfXhWPbz589h6NCH60csWvQpAODFF7tj+vRZRuVGiL3QT7YGgBalI6QCYdyBf9uTkjLKbPIVBAZ3dxdIkmRX90kfBYzpbollZeWZNZTuUaWKOg3fDs/i/v5D0IQ3dqj4ls7dGILAgHOn4d+mNVIPHIY2ojE0D9Y5U6lh0rUmCMzk1ypxDMyNYcrr1Op8JCffgb9/VTg5OT98IjsbqssXoQmtD1QybX0aQpRQ4rUJ3d+NgADjbl9W6Fs8hBDi6Bz4OyZ5RCl1TVIFhRCFcZUTtFWrgauKLm9u7/EtnbvRnJygrVZNt+hdIbqWO2ax/ihKHANzY5jyOv0UBvn5hfqaMAbu5GzaEs6EKEh/TYpi+XqR0C0eYja6xUMsofBtGQDyYycNACcBai7p5t8oY4Vic27xOJK0tGTk5GTCw8MXzs4uNK8RsSnOOfLz85CZeR9ubh7w9i66no8pt3gqdCdZQoij0XWYjctKR5C7V4XvNOvlpZvBNDPzfhl7EmI9bm4e8rVZHlRBsZCpUz9CRkYGFi1aYutUiJWJ58/B+/VXkLZ9N7RPNHCo+JbO3Viqc+fg06snMnd+A83jRfPQSJabgUyJY2BuDFNfxxiDt7c/PD19odXqpi8Xrl6B53tjkLFgKaS69czKnxBziaLK5Gn6S1KhKyhZWVlYuXI5fv/9IO7fT0FYWH28//5Eg1UcOedYvXol9uzZjYyMDERENMbkyR+hZs1aAID4+Nt44YXnsX37LnkyNSWVVX5xPv98PQ4ePICYmBtwcXFBRERjjBkzTp7jJC0tDatXr8SxY3/j7t278PX1xXPPtcOIESPh6Wm7ybkeFUyjhngnHkxj/MrV9hLf0rkbnYdaDTE+HgyasndWumwFjoG5Mcx9nSAIEATdaAmVWg33kyeRr1ZDU2gEBSGOpEJ3kp0xYxqOHz+GmTNnY+fO3XjmmRYYMWIo7t1LkPfZvHkjtm/fhsmTP8bmzV/Czc0NI0cOR56VJkEyp/x///0Hr77aB5s3b8WqVWuh0Wjw9tvDkZOTDQBITLyHxMR7GDduPHbt+gbTps3E33//hRkzplrlPRFSFn1fCg3n1K+CkAqqwlZQcnNzcfDgfowd+w6efPIp1KxZE8OHv42goBr46qtdAHStF9u2bcXgwUPw3HNtERoaihkzZiMxMRF//HEQAPDCC7p1fF5//VU0bRqOIUP+Z1DOF19sQqdO7dC2bWvMnTsbarXx34yMKb84K1asRvfuLyEkpC5CQ8MwffpM3L17B+fP65YEr1u3HhYs+Axt2jyHGjVqoFmz5hg5cjQOHfrTYJVLQqyNMVCFhBACoAJXULRaLbRaLZydDZtAXV1dcfr0KQC6KeKTkpIM1q3x9PREw4aNEBV1BgCwZYtuFcpVq9bi118PYsGCz+R9//nnJOLiYrFmzQZMnz4L33//Hb7//jv5+dWrV6Jbty4l5mhM+cbIyMgEAHh7e5e4T2ZmBtzdPYpZ4JAQ69BPaa91QgX+ZCKE6FXYjwF3d3eEh0dg/fq1SEy8B61Wix9//AFRUWeQlJQIAEhOTgIA+PkZDpXy9/dHUlIyAMDX1xeAbn2bgIAAg0qAp6cXJk6cjNq1a+PZZ9ugdetnceLECfl5Hx9fBAUFlZijMeWXRZIkLFgwH40bN0HdEjrM3b9/H+vWrUXPnq8YFZOUTlsnBKl7foS2TojDxbd07qXRT2kfn50Bbd0QpPz0E7Qh1s9DiWNgbgxblk2IvanQX5dnzpyD6dOnoHPnDhBFEfXrP47OnZ/HhQvnFYkfEhIiT6YEAAEBAbhy5Yr8uE+f19Gnz+uKlFWSefNm49q1q/j8803FPp+ZmYmxY0eiTp06GDZshEVzqSi4hyfUrVo7ZHxL524MraQF9/aB+tnWYBIAK/fXVeIYmBvDlmUTYm8qbAsKANSoUQPr12/EX38dw08//YotW7ZBo9HIrRr+/gEAgJQUw9aK5ORkBAQUnYCmsKK3S0ybs6G85c+bNweHDx/C2rXrUaXKY0Wez8rKwqhRI1CpkjsWLlwMp2Jm7SSmE+7Ew33WNAh34h0uvqVzNzqP+Hh4TJ0KId76eShxDMyNYcuyCbE3FbqCoufmVgmBgYFIT0/H0aN/o02btgCA6tWrIyAgACdOHJf3zczMRHT0WYSHRwCA/Eddq5UUz8uY8ovDOce8eXPw++8HsWbNelSvXvQ2UmZmJt5+exicnJzw2WdLDVZHJuUjJN5DpaWLICTec7j4ls7d6DwS7sF94SII96yfhxLHwNwYtiybEHtToW/x/P33X+CcIzg4GLGxsVi8eBGCg4PRvftLAHT3xN94ox/Wr1+LmjVrolq16li1agUCAwPx3HPtAAC+vn5wdXXF338fQZUqVeDs7Gz0XCI7dmzH778fwJo164t93pjyAWDYsMFo27a9fLto3rzZ+Pnnn/HZZ0tQqZI7kpJ0fVk8PDzg6uoqV05yc3Mxa9ZcZGVlISsr68H78TW4LUUIIYTYQoWuoGRmZmL58iVISEiAt7c32rXrgJEjRxvc6hg48C3k5ORg1qwZyMjIQOPGTbB8+Sq5xUGlUuH99ydi3bo1WL16JZo0aYp16z43qvzU1PuIi4srdZ+yygeAuLg4pKY+nOpaP0y68JDnadNmonv3l3Dx4gVER58FALz0UjeDfX744WdUq1bdqPwJIYQQS6nQFZROnTqjU6fOpe7DGMOIESMxYsTIEvd5+eVX8PLLhiNgpk+fVWS/99+faPB4+PC3MXz42+Uu/8cf9xk8/u+/qFJjPvXU02XuQ4i1mDvtCSvHawkh9o/6oBCiMMnXDzl9B0DyLf9iWdaOb+ncC9PPfSK4GN5WlPz9kDNwACS/4vMQGYOoEsCcBSi07MfDshU4BubGsGXZhNgbxh14KdCkpIxSl1oHdMutu7u7QJKkMvclpmFMtwZIVlbeI7eMPbEOQWDQPLij6qRhUKs4bmeloYa3DyTOwSRApdZtL7xPkJc3VIIApgW0udpif78LxlepQdcpITbGGBAQYFw/TWpBIURpOTkQL14AcnIcL76lcy9Bkds1OTkQz5eeB2MMakmChksQBAXv9ShxDMyNYcuyCbEzVEEhRGGqK5fg92xzqK5ccrj4ls69IH2FhAEPbtcw6KsZqouXENCsGVSXSs/j4WsFxfqjKHEMzI1hy7IJsTdUQbGQqVM/wrvvjrV1GoTYpYd9TwQIjEHDJWg4B0ysZOhbUdRcokUGCXnEVOgKSlZWFj799BN07doZLVo8jTff7I9z56IN9pk69SM0bRpu8DNy5HD5+fj422jaNByXLl20SI6cc6xatQKdOrVDixZPY/jwIbh162apr/n88/Xo1+91REY+g/bt2+Ddd8ciJuaGwT5JSUn46KPJ6NixLVq2bIY33ngVBw78ZpH3QEhh+nV3zKmUEEIqhgo9zHjGjGm4du0qZs6cjcDAyvjppx8wYsRQfP31HlSuXEXer2XLVpg2bab8uPAKyJa0efNGbN++DTNmzHowUdtyjBw5HF9//W2Js7/+++8/ePXVPmjQoAG0Wi2WL1+Kt98ejt2798DNrRIAYMqUD5GRkYHPPlsKHx9f7Nv3EyZOfB9bt25H/fqPW+39EVIWxnR1GOreSkjFUmFbUHJzc3Hw4H6MHfsOnnzyKdSsWRPDh7+NoKAa8kRnes7OzggICJB/vLy85OdeeOF5AMDrr7+Kpk3Di0yO9sUXm9CpUzu0bdsac+fOhlpt/MpnnHNs27YVgwcPwXPPtUVoaChmzJiNxMRE/PHHwRJft2LFanTv/hJCQuoiNDQM06fPxN27d3D+/MNFEM+cOY3XXnsdDRs2QlBQEAYPHgpPT0/FFkqs0BgDd3a23CQdloxv6dxNzIMxBubMAJFZr6FFiWNgbgxblk2InamwLSharRZarbZIa4irqytOnz5lsO2ff/5B+/Zt4OXlhaefboa33x4NHx8fAMCWLdvQv/8bWLVqLUJC6hrMQvvPPycREBCANWs2IDb2FiZNeh9hYWHo2bMXAGD16pX4/vu9RSZa07t9+zaSkpLQvPkz8jZPT080bNgIUVFn0Lnz80a914yMTACAt7e3vC0iojF+/fUXtG79LDw9PfHbb78gLy8PTz75tFExSck0jSKQFJfkkPEtnbvReTSOwL2UZIicAZyDM261v7dKHANzY9iybELsTYWtoLi7uyM8PALr169FnTp14Ofnj337fkZU1BnUqFFD3q9ly1Zo1649qlWrjri4OCxfvhSjR7+NTZu2QBRF+Pr6AgB8fHwQEBBgUIanpxcmTpwMURRRu3ZttG79LE6cOCFXUHx8fOWVk4uTnKz7kPHzM1y52N/fH0lJycW9pAhJkrBgwXw0btwEdevWk7d/8smnmDhxAtq2bQ2VSgVXV1csXLgYNWvWNCouIYQQYkkV9hYPAMycOQecc3Tu3AHPPPMUduzYhs6dnwdjDw9L587Po02btqhXLxRt27bDkiXLce5cNP7552SZ8UNCQgwW3gsICEBKysOKRZ8+r5e4UKBS5s2bjWvXrmLu3E8Mtq9cuQKZmelYtWottm7djr59+2PixPdx5cpli+ZTEYiXL8GnfWuIly0zzNOS8S2du9F5XLwIv1aREC9apvN5qWUrcAzMjWHLsgmxNxW2BQUAatSogfXrNyInJxuZmVkIDAzExInvl9qqERQUBB8fX8TGxhrceimOSlX48DKYMnGvv7+uRSYlJRmBgYHy9uTkZISFhZX5+nnz5uDw4UNYv34jqlR5TN4eGxuLnTu346uvvkFISF0AQGhoGE6d+g+7du3Ehx9+bHSOpCiWmwOns2fAci0zUZYl41s6d6PzyMmF05kzQG6u6a99cCvI3JmjlTgG5sawZdmE2JsK3YKi5+ZWCYGBgUhPT8fRo3+jTZu2Je6bkHAXaWmpCAzUVR70fU60WknxvKpXr46AgACcOHFc3paZmYno6LMID48o8XWcc8ybNwe//34Qa9asR/XqhhWu3AcfXAVbigBAEERIkvLvgxBL089EKwiA+GBtH+ojSohjq9AVlL///gt//XUEt2/H4dixoxg6dBCCg4PRvftLAIDs7Gx89tlCREWdQXz8bRw/fgzvvDMWNWrURIsWrQAAvr5+cHV1xd9/H0FycjIyMjKMLn/Hju0YNmxwic8zxvDGG/2wfv1a/Pnn77hy5TKmTPkQgYGBeO65dvJ+w4YNxo4d2+XH8+bNxk8//Yg5c+ahUiV3JCUlISkpCbkPvo0GB9dGjRo1MXv2DERHn0VsbCy2bNmM48ePom3bdkXyIMSeFZxNVnQRoRY4NDRxGyEOr0Lf4snMzMTy5UuQkJAAb29vtGvXASNHjpZbRQRBwJUrV/DDD3uRkZGBwMDKeOaZFnj77VHy6B+VSoX335+IdevWYPXqlWjSpCnWrfvcqPJTU+8jLi6u1H0GDnwLOTk5mDVrBjIyMtC4cRMsX77KYA6UuLg4pKbelx/rh0kXHvI8bdpMdO/+EpycnLBs2QosXboY48aNRnZ2NmrUqInp02chMrK1UbkTYi/0s8mKDwYic1hvxA8hxHJoNWNiNlrNuHgs9T6cDv0B9bPPgfv4OlR8S+eup19lWGAMjAFaxiFyBolzxGWmoabEoTr4O7TPtQX39YWWGa5srF/xWE/iutcDkPcV83XPmbqasRLHwNwYtiybEGswZTVjqqAQs1EFhZirrApKDW+fYisd1qigEEIsx5QKikl9UNasWYNXXnkFTZo0QYsWLfD222/j+vXrBvvk5eVh+vTpaN68OZo0aYLRo0cjKclw0qD4+HgMHToUERERaNGiBT755BNoNBpTUiHEbrF79+C2ajnYvXsOF9/SuRudR0ICKi1bBnYvwfplK3AMzI1hy7IJsTcmVVBOnDiBvn37YteuXdi4cSM0Gg0GDRqE7OxseZ85c+bg999/x+LFi7Flyxbcu3cPo0aNkp/XarUYNmwY1Go1duzYgXnz5mHPnj1YunSpcu+KEBsS78bDY+pkiHfjHS6+pXM3Oo/4O/D8YDLE+DvWL1uBY2BuDFuWTYi9MamCsmHDBvTs2RP16tVD/fr1MW/ePMTHx+PcuXMAgIyMDOzevRuTJk1CixYt0LBhQ8yZMwenTp3C6dOnAQBHjhzB1atX8emnn+Lxxx9HmzZtMHbsWHz55ZfIz89X/A0SQhyXIABMoB6vhFRE5RpmrB9Sq1/jJTo6Gmq1Gi1btpT3CQkJQbVq1eQKyunTpxEaGmowLXxkZCQyMzNx9erV8qRjV1avXok+fXrbOg1CHJqWAxKtY0xIhWR2BUWSJMyZMwdNmzZFaGgoACApKQlOTk4Gq/0CurVjEhMT5X0Kr1mjf6zfx1qysrLw6aefoGvXzmjR4mm8+WZ/nDsXbbBPdnY25s2bgy5dOqBFi6fxyis98PXXhqsdN20ajt9/L3l14fLauXMHunXrgmeeeQoDBryB6Oizpe5/4MB+9O3bB88+2wotWzZDnz698cMP3xvsM3XqR2jaNNzgZ+TI4RZ7D4QQQogpzJ4HZfr06bhy5Qq2bdumZD5WNWPGNFy7dhUzZ85GYGBl/PTTDxgxYii+/noPKleuAgBYuPBTnDx5ArNmzUW1atVw9OhRzJs3G4GBgaXOOKuUX37Zh0WLPsXkyR+jUaNG+PLLrRg5cjj27NlbZBFBPW9vbwwaNATBwbXh5OSEw4f/xPTpU+Dn54eWLVvJ+7Vs2QrTps2UHxde2ZmYR/L0Ql7n5yF5epW9s53Ft3TuRufh7YW8rl0heVs/DyWOgbkxbFk2IfbGrBaUGTNm4I8//sDmzZvx2GMP13gJCAiAWq1Genq6wf7JyQ/XkgkICCgyqkf/uOB6M5aWm5uLgwf3Y+zYd/Dkk0+hZs2aGD78bQQF1ZAnOgOAqKjTePHF7njqqadRrVp1vPJKL9SrF4roaF1LS7duXQAA48ePQ9Om4fJjvR9++B7dunXBs8+2xKRJE5CVlWVSnl9++QVefvkVvPRSD9SpE4IPP/wYrq5u+O67b0t8zVNPPY127dqjTp06qFGjBt54ox/q1auH06dPGezn7OyMgIAA+adwyxcxj1S7DtK37IRUu47DxbdUbMZg0uRpUp06SP9qF6Q6ljmGpZatwDEwN4YtyybE3phUQeGcY8aMGfjtt9+wefNm1KhRw+D5hg0bwsnJCUePHpW3Xb9+HfHx8WjcuDEAoHHjxrh8+TKSkx+u6vv333/Dw8MDdevWLcdbMY1Wq4VWqy3SauDq6mrwhzw8vDH+/PMP3LuXAM45Tp48gVu3buKZZ1oAALZu1bUgTZs2E7/+elB+DABxcbH444+DWLJkGRYvXob//vsHGzdukJ/fu/c7NG0aXmKOarUaFy5cMFiUUBAENG/eHFFRZ4x6n5xzHD9+DDExMWja9EmD5/755x+0b98GL7/8IubMmYnU1FSjYpIyqNVgSUmAWu148S0QmzFAMHV9HLUaLDHRcsewrLLLewzMjWHLsgmxMyZVUKZPn469e/di4cKFcHd3R2JiIhITE+U1Xjw9PfHKK69g3rx5OHbsGKKjozF58mQ0adJErqBERkaibt26mDBhAi5evIjDhw9j8eLF6Nu3r1VvMbi7uyM8PALr169FYuI9aLVa/PjjD4iKOoOkpId9YSZO/AB16tRBly4d0bz5kxg1agQmTZqMJ598CoBuLR79ew8ICJAfA7p+OtOnz0LduvXQtOmT6Nr1BYOF/zw8PBAcHFxijqmp96HVaovcyvHz80dyclIJr9LJyMhAq1bN0bz5kxg7dhQmTPhArlQButs7M2fOwurV6zBmzDv4999/MXr029BqtWUfPFIq1YVzCHiiDlQXzjlcfEvEZoxBzSWoTVgfRxV9DgHBtaGKtswxLLVsBY6BuTFsWTYh9sakPijbt+sWpOvfv7/B9rlz56Jnz54AgMmTJ0MQBIwZMwb5+fmIjIzE1KlT5X1FUcTq1asxbdo0vPbaa3Bzc8PLL7+MMWPGlPe9mGzmzDmYPn0KOnfuAFEUUb/+4+jc+XlcuHBe3mfHjm04ezYKn322FFWrVsN///2LefPmIDCwskHLRnGqVasOd3d3+XFAQCDu30+RH7dr1x7t2rVX/o1BVwHbvv0r5ORk48SJ41i0aAGCgoLw1FNPAwA6d35e3rdevVDUqxeK7t274p9/Tpb5vgixBBpMTAgpyKQKyqVLl8rcx8XFBVOnTjWolBRWvXp1rFu3zpSiLaJGjRpYv34jcnKykZmZhcDAQEyc+D6CgoIA6PqpLF++FAsXLkbr1s8CAEJDQ3H58kV88cWmMv+Qq1SGh5cx06ba9vHxhSiKSElJNtiekpIMf/+AEl6lIwgCatasCQAIC6uPGzeu4/PPN8gVlMKCgoLg4+OL2NhYqqAQq2MABJFBkmydCSHEXpRrHpRHhZtbJQQGBiI9PR1Hj/4tj87RaDTQaDQQCk0UJQgiCi5hpFKpIEnK3xpxcnLC448/bnBbSJIknDhxHOHhESbFkiQOtbrkifASEu4iLS0VgYGlV3wIsQTGGDScQ6KViAkhD1ToCsrff/+Fv/46gtu343Ds2FEMHToIwcHB6N79JQC6PiJPPvkUFi9ehH/+OYnbt+Owd+93+PHH79G2bTs5TrVq1XHixHEkJSUVGcFUmoMHD6Bnz+6l7tO37wDs2bMb33//Ha5fv445c2YhJycH3bv3kPf5+OPJWLZsifz488/X49ixo4iLi8P169exZctm/PTTD+jatRsA3dwun322EFFRZxAffxvHjx/DO++MRY0aNdGiRavCKRBiFxhMGwlECHFsZs+D8ijIzMzE8uVLkJCQAG9vb7Rr1wEjR46Gk5OTvM/cufOxbNkSfPjhB0hPT0PVqlUxcuRo9Or1qrzPO++Mx6JFC7BnzzcIDKyMH3/cZ3T5MTExpe7TuXMX3L9/H6tWrURychLCwsKwfPkq+Ps/7Dh79+5dCMLDumZOTg7mzp2Ne/cS4OLiguDg2pg5cw46d9YNgRYEAVeuXMEPP+xFRkYGAgMr45lnWuDtt0fRXCgK0DRohKRrceCV3Mve2c7iWzp3o/MIb4SkO/HgD/pwMQCiSgATAKa17MyyShwDc2PYsmxC7A3jBe9VOJikpAyUlb0gMLi7u0CSpDL3JaZhTFfZycrKo2XsSYkEgUHzoM6vUutuN+q3CYyBMYA/qF9LnCM+Iw1BXj5F4kicQ+QMEueIy0xDDW+fIs8BgJZxMAkQH9zRLFw2IcR2GAMCAjyN2rdC3+IhxBLE61fh/WoPiNcts7aUJeNbOnej87h6Fd7dX4JowvpcSt39UeIYmBvDlmUTYm+ogkKIwlhmJpz/OAiWmelw8S2du9F5ZGTC+cABsAzj8tCPAlJi5WMljoG5MWxZNiH2pkL3QSGEPBr0o4AIIY8OakEhhFQIpq4HRAixLaqgEEIeeQwAcxZMWw+IEGJTVEEhRGHaakHImLsA2mpBDhff0rkbnUdQdWQsWghtUHVF4gkmrAekxDEwN4YtyybE3tAwY2I2GmZMjGGNYcaF9ys4zFjrBKhUArScg2s5RBpuTIjN0DDjYjH5HjT9KPNDy7sVj91PgctXO8AKLAzpKPEtnbvReaSkwGX7DrCU8ufBGIMWumn0jblklTgG5sawZdmE2JtHvoLCOYckSRAEBkEQ6EfRH/agZYq+jRYkxt6C18ihEGNvOVx8S+dudB43b8Fr8GCIN62fhxLHwNwYtiybEHvzyA8z5hzIzs4v874zMQ/nnG6dEUIIUdwjX0EBdJUU+pZPCCGEOI4KUUEhhFgfNVoSQsqDKiiEKIxXcof6yacttpqsJeMrFZsxQHARdTHzJdPzcK8EdbNm4O6VypWHOZQ4BubGsGXZhNibR36YMSHE+goOLXbSMKhVul9UWwwzlpwZIBpup2HGhNgGDTMmhNg9ugNECCkNVVAIUZgq6jQCK3tBFXXa4eJbOndAf/tHAERWYiVFdeo0At09oDplXh4MMHvknhLHwNwYtiybEHtDfVAIIValX3mYM26RnrQMgKgSoIEEJybohsIrXgohxNKoBYUQYjW6WYhLbjkp8/VGlcGgliTEZaVDw42bPZYQYn+ogkIIsQr9isJaJwAik7eZ8npBZGCCca/SSFpTUySE2BGqoBBCrII9WFE4PjsDHNzkCof+1pAETnOsEFIB0DBjQpSWmwsh/jakatUBV1fHiq9Q7OKGGQuMgaPsIcLxGWkIcnaFcPs2pOpF8yhtKHLBfW6m3UdtTz95GLPRw4yVOAbmxrBl2YRYgSnDjKmCQghRXLkrKGVUPpSqoOhbYuhzhBDroHlQCLEh4WYMPEcMhnAzxuHiWzp3o/OIiYHn/wZBiLFcHvrZbgUX0eCWkRLHwNwYtiybEHtDFRRCFCakpcJ19y4IaakOF9/SuRudx/1UuO7cCeG+8nnoRhI97BOj5pLBnClKHANzY9iybELsDVVQCCEVhn4kkX6dIEKI/aKJ2gghFYa+1YRzwIkxgKZwI8RuUQsKIeSRRaORCXFc1IJCiMKkKo8h671JkKo85nDxLZ270Xk89hiyJn8A6THz8xAZgyAycACmTHavxDEwN4YtyybE3tAwY0KI4uxhmHHhfSTOIXIGiXPdLZ4HeQGASl3G3CiEEEXQMGNCbIhlpMPp4H6wjHSHi2/p3I3OIz0dTr/tB0u3fh5KHANzY9iybELsDVVQCFGYeOM6fPr0hHjjusPFt3TuRudx7Tp8evSAeM36eShxDMyNYcuyCbE3VEEhhBBCiN2hCgohpMKgUT2EOA6qoBBCKoSCqydTRYUQ+0cVFEIUxp1doA2uDe7s4nDxLZ270Xm4OENbpw64i7NiMRlj0HAOCbzUphQljoG5MWxZNiH2hoYZE0IUpV/nxt6GGRd8jkmASk3DjAmxNlOGGdNEbYQQxehXCGYMYJJEE8kTQsxGt3gIUZh4Lhr+j9eGeC7a4eKXN7Z+rRsNL/02Spl5nI2Gf61aEM9a5hiWWrYCx9fcGLYsmxB7QxUUQhTGtBoIyclgWo3DxbdobFP21WggJCWDaSxzDEstW4FjYG4MW5ZNiL2hWzyEEIvTj6ChPmOEEGNRCwohxOIYY9BCN4KG0RhfQogRqIJCCCGEELtDw4wJUVpmJlQXzkHzeAPAw8Ox4pcztn4VY/2QYv2qwVonAGLZQ4Tl5wUVVOfOQdOgaB4WH2asxPE1N4YtyybECkwZZkwVFEKIYhSroNA8KIQ8kkypoNAtHkIUJsTfhvvHH0CIv+1w8c2JrZuYTeE8bt+G+8RJEG5b5hiWWrYCx9fcGLYsmxB7QxUUQhQmJCWi0poVEJISHS6+qbH1E7PpJ2craR9T6y/CvURUWr4cwj3LHMNSy1bg+Jobw5ZlE2JvaJgxIcRs+onZAEBVTA2FAWDO+hoK3UIhhBiPWlAIIRajX6CP21nlhEGXG414JsR+UQsKIaRCYQBElQAt4wBjYFr7qjwRQnSoBYUQhUl+/sh5azAkP3+Hi29sbEt0jDXII8AfOUOHQApQ/j0yxqCWJMRnZ+hadgq9DyWOr7kxbFk2IfaGhhkTQkyi7xgLADxfgsZJ9zdepQE4h8EwYwYGxgD+4KuQPQwzLvh8NU9vMAkQ82mYMSHWQMOMCbGl7Gyook4D2dmOF9+I2PqOsWouyf04RJUA5qzgx0l2NlSnSs/DYpQ4vubGsGXZhNgZqqAQojDV1cvw7fAsVFcvO1x8c2LrOsI+rLAoksely/CNjITqkmWOYallK3B8zY1hy7IJsTdUQSGEVHiW7lNDCDEdVVAIIRWabq4WodTJ5ggh1kfDjAkhimNwnGnZ9H1qOC9+sjlCiG1QCwohCuNMgOThCc4s8+tlyfhKxGYABJGBCeZPhMYFAZKnJ7hg/Y8oJY6BuTFsWTYh9oaGGRNCTKJfsRh4uFKxJDxcIbjgysUi11VR7HmYscgZJK5beZlWNSbEsmiYMSGEEEIcGlVQCFGYeOkifFs3g3jposPFNzW2OSsVG5XHhQvwfeopiBcuWCB6GWUrcHzNjWHLsgmxN9RJlhCFsbxcqC5dBMvLdbj4psQ2WKlY4XutLDcPqgsXwXLzFI1rVNkKHF9zY9iybELsjcktKCdPnsTw4cMRGRmJsLAw7N+/3+D5SZMmISwszOBn0KBBBvukpqZi/PjxaNq0KZ566ilMnjwZWVlZ5XsnhBCrMmalYkcbE0ODeAixHya3oGRnZyMsLAyvvPIKRo0aVew+rVu3xty5c+XHzs7OBs+/9957SExMxMaNG6FWqzF58mRMmTIFCxcuNDUdQoid0o/mkThKrcTYC3k+lAJT9gsCA3/QgZYQYl0mV1DatGmDNm3alLqPs7MzAgMDi33u2rVrOHz4ML7++ms0atQIAPDRRx9h6NChmDBhAqpUqWJqSoQQO6RvYXEU+vlQxAc5C84C8p0AJyZCytNSJYUQK7NIJ9kTJ06gRYsW6Ny5M6ZOnYr79+/Lz506dQpeXl5y5QQAWrZsCUEQEBUVZYl0CLEqba1gpH2xA9pawQ4X39K5G51H7WCk7doJbW3r56ENDkbG1p3Iq1ULcVnpZq0xZO5xVOL428s5JKS8FO8k27p1a3Ts2BFBQUGIjY3FokWLMGTIEOzcuROiKCIpKQl+fn6GSahU8Pb2RmJiotLpEGJ13NsH+V26OmR8S+dudB4+Psjv1s2qZepnv+U+PlA/3w1cxaHJuF/Wy4pl7nFU4vjbyzkkpLwUb0Hp1q0b2rdvj7CwMHTo0AFr1qzB2bNnceLECaWLIsQusYQEuC1ZCJaQ4HDxLZ270XncTYDbpwvA7lonj4Kz34oJCXD9bAGEchwDc4+jEsffXs4hIeVl8XlQatSoAV9fX9y8eRMAEBAQgJSUFIN9NBoN0tLSSuy3QogjERPuwGP2dIgJdxwuvqVzNzqPO3fgMW0axDvWyYMxBi04JHAId++g0qxpEMpRtrnHUYnjby/nkJDysngF5e7du0hNTZUrH02aNEF6ejqio6PlfY4dOwZJkhAeHm7pdAghhBDiAEzug5KVlYVbt27Jj+Pi4nDhwgV4e3vD29sby5cvR+fOnREQEIDY2Fh8+umnqFWrFlq3bg0ACAkJQevWrfHxxx9j+vTpUKvVmDlzJrp160YjeAghhBACwIwKSnR0NAYMGCA/1s938vLLL2PatGm4fPkyvv32W2RkZKBy5cpo1aoVxo4dazAXyoIFCzBz5kwMHDgQgiCgU6dO+OijjxR4O4QQW2HQ3SphDjHrCSHE3plcQWnevDkuXbpU4vMbNmwoM4aPjw9NykYeWZKXN/Je7AHJy9vh4psbmwEQVQK0jAOCfjxMOfLw8Ubeyz0g+VjmGJZatrc38rv3APc2LFs/0tiY+VDMPY5KnFtLX3+EWAvj3HGnH0pKyqDJkwixMkFg0DgBAmNgDOAPerJJnCMhKxNVPUpeSl3iHPEZaQjy8jHreaX20T9fzdMbItfVPLig284kQKVmUKs4YjLuI9jTF04aAE66N0qTthFiPsaAgICSPyMKotWMCVFafj6E+NtAfr7jxTcyNkPx6+xoJa1yedy24DEso2xWpGzdLLNGT9pm7jlS4txa+vojxEqogkKIwlQXz8O/8eNQXTzvcPHLis0YILgIYCKD+GDeEEtQnTsP/9AwqM5Z5hgWVPgdqM6fh294GFTnzS/b3HOkxLm19PVHiLUoPpMsIeTRJa+vIwCaR6ArrKMtaEhIRUIVFEJIheVoCxoSUpHQLR5CiEksc1OHEEIMUQWFEGIUff8TiFRFIYRYHg0zJkRpkgSo1YCTEyBY4DuAJeOXElsQGCRnBoglvFTB4b9BHl4l5qFoOYWelzgH00hQZWugdlMhJivtwTBj3bBjAFCpAUkq44PH3HOkxLm19PVHSDmYMsyY+qAQojRBAFxcHDO+pXN3gDyYIIC5ugBCOb79mJu/Eu/bXs4hIeVE1WtCFCZeuwLvHl0hXrvicPEtnbvReVy5Au8uXSBesW4eDIDTjWvw7P48VFfNL9vc46jE8beXc0hIeVEFhRCFsawsOP99BCwry+HiWzp3o/PIzILz4SNgmdbNgzEGbUYGVEcOl+sYmHsclTj+9nIOCSkvqqAQQgghxO5QBYUQQgghdocqKISQEjH2cBVfQgixJhrFQ4jCtNVrIGPRMmir13C4+AVj6+Y90Y0plvIUWgTQ2DxqBCFjxXJoawRZtVwA0AYFIWNZ+co29xwpcW4tff0RYi00DwohpFiCwKBx0v1fpdb9a7V5UMoRo7zlSJxD5AwS57iRft+8eVAIIcUyZR4UusVDiMJYcjJct24GS052uPiWzt3oPJKS4LppE1hSku3KTja/bHOPoxLH317OISHlRRUUQhQm3o6F57ujId6Odbj4ls7d6Dxi4+A5chTE2Dirl62Ki4PH6FFQ3b5tdgxzj6MSx99eziEh5UUVFEIIKYA9WA5RAqcOwoTYEFVQCCGkFAw0kokQW6AKCiGElEBkDKJKAHPWt6sQQqyFhhkTojDu7o78lpHg7u4OF9/SuRudh4c78ltHgntYPw+5bHd3CIxBwyVwznRNKaUM3tG3snBu/nFU4vjbyzkkpLxomDEhpFgVdZhx4X2qeXrLw445L36YceE5Y+hziZDi0TBjQmxJkoC8PN2/jhbf0rk7Qh5mlM0Yg5pLUHMJjDHz81fifdvLOSSknKiCQojCVNFRCKwRCFV0lMPFt3TuRudxJgqBfv5QnbF+HnLZUeaXbe5xVOL428s5JKS8qIJCCCGEELtDFRRCCCGE2B2qoBBCCCHE7lAFhRBCCCF2h4YZE6K0/HwISYmQAgIBZ2fHil8gtuDqYrthxq6VICQmQgos+h4tPsw4Px9CYiI0AQGIz8sxaphx4SHZUm6eeedIiXNr6euPkHIwZZgxTdRGiNKcnSFVq+6Y8S2duyl5VLdRHvqyOQfycsyPYc5xVOL428s5JKSc6BYPIQoTYm7Aa9AACDE3HC6+pXM3Oo8bN+DVrx+EG9bPQ1+2WI6yzT2OShx/ezmHhJQXVVAIUZiQngaX77+FkJ7mcPEtnbvReaSmwWXPtxBSrZ+HvmyWZn7Z5h5HJY6/vZxDQsqLKiiEkFLRar4P0bEgxHqogkIIKUJwFiC4CBCAB6v50kcFw8NjQZUUQiyPOskSQorQcA6JczBBt5ov44Cqgv9VZuzhsRAZgwMPgCTEIdDXIkIUpq1SFZkfToW2SlWHi6+tUhVZH02FVNUyuRudR9WqyJw2DVob5KEvuzzHwNxzpMS5tfT1R4i10DwohBAD+jk9BMbAGKBlHIIEqDQCtCoObo15UMoRQ+ly9POgALpjwSRAzDecC6XIPCgSfTARUhxT5kGhFhRCFMbSUuG87yewtFSHi8/SUuH0849gqQ9j6/teaJ04IFrnNg9LTYXzj4Z5WIsSZZt7jpQ4t5a+/gixFqqgEKIw8WYMvAf0gXgzxuHiizdj4NW/D8SYGOirIowxqCUJ8dkZ4LBOy4B4Iwber74G8UaMVcortuwY88s29xwpcW4tff0RYi3USZYQUoQAgIsMHJArJVpJa9OcCCEVC1VQCCFFiQxaK7WWEEJIcegWDyFExir4UGJCiP2gCgohCuMurtCE1Qd3cXW8+K6u0NSvD+7qonxsE3BXF2get00ectnlOL7mniMlzq2lrz9CrIWGGRNCZILAIDkzoNBQYnsc/kvDjAlxPDTMmBBiNMZofRlT0TEjxPKogkKIwsSzUfCvUx3i2Si7j88YILiIEFxEMPYgdvWqEM9YJndjiWei4P+YbfLQl62KKr5sBoA5C/IxKzaGmedIiXNr6euPEGuhUTyEKIxxCUJmBhiX7D4+YwzqB3FUjAGSBCEjA0yyTO5G52XDPPRlo4Sy9ceMP1ifqLi75OaeIyXOraWvP0KshVpQCKlA6NYEIcRRUAWFkAqi8O0cQgixZ3SLh5AKovDtHAcewEcIqQCoBYUQhWnqhuL+/kPQ1A11uPjaeqG4f+gINGGWyd1YmrBQ3D9imzzkskPNL9vcc6TEubX09UeItVALCiFKq1QJmvDGjhm/UiVoGjcuMg+K1VWqBE2TxrYtm3MgI83olzHg4eIA5p4jJc6tpa8/QqyEWlAIUZgQFwuPie9CiIt1uPhCXCw83n0HQqxlcjc6j9hYeLxjmzxMLVvXt0cARCavAG3uOVLi3Fr6+iPEWqiCQojChJRkuG1cDyEl2eHis+RkuK1fByHJMrkbS0hKhtta2+Qhl538sOzS+hQzxqDhXLfq84MdzT1HSpxbS19/hFgLVVAIIaQUDIAgMjCBhj4RYk3UB4UQQkqhbyExh+AsgDHQmmGEmIFaUAghRGHswUQzGs7l/xNCTEMVFEIUJgUEInvYSEgBgQ4XnwcGInvkKEiVLZO7saTKgcgeZZs85LIDzS+bBwYia+RIk2MocW4tff0RYi2MO/BsTUlJGdR0SoiRBIFB46T7v0oNSBIvsg0AJGdWZJixxDniM9IQ5OVTYnwl9rH3ciTOIXIGiXPdWjwFjpkkcDAJEPN12wofa0KIbtRbQICnUftSCwohSsvMhOrkcSAz0/HiZ2ZCddyCuTtCHg/KZuUpOzMTTifMiKHEubX09UeIlVAFhRCFqa5fhW+3jlBdv+pw8cVrV+HbsT1UVyyTu7FUV67Ct51t8tCXLV41v2zx2lX4dexgcgwlzq2lrz9CrIUqKIQQQgixO1RBIYQQQojdMbmCcvLkSQwfPhyRkZEICwvD/v37DZ7nnGPJkiWIjIxEeHg43nzzTcTExBjsk5qaivHjx6Np06Z46qmnMHnyZGRlZZXrjRBCCCHk0WFyBSU7OxthYWGYOnVqsc+vW7cOW7ZswbRp07Br1y64ublh0KBByMvLk/d57733cPXqVWzcuBGrV6/GP//8gylTppj/LgixI1xUQfL3BxctMw+iReOrHsRW2XYOR65SQQqwTR76slGesh8cR1NjKHFuLX39EWIt5RpmHBYWhhUrVqBDhw4AdK0nrVu3xltvvYVBgwYBADIyMtCyZUvMmzcP3bp1w7Vr19C1a1d8/fXXaNSoEQDg0KFDGDp0KP78809UqVLF6PJpmDEhxittmDEDoNIwcM5pmHEp+9AwY0LKx2bDjOPi4pCYmIiWLVvK2zw9PREREYFTp04BAE6dOgUvLy+5cgIALVu2hCAIiIqKUjIdQogRGABRJUDjxCG4CKUujEcIIdaiaAUlMTERAODv72+w3d/fH0lJSQCApKQk+Pn5GTyvUqng7e0tv54QRyZevAC/ZhEQL15wiPi6tWYkxGWlg58/B9/G4RDPn1cktrnE8+fh18g2eTws2/zjK168AP/GERAvmBZDiXNr6euPEGuhUTyEKIzl50GMuQGWn1f2zjaMX3iJGI2kBcvLh3j9Olhefrlil5ct85DLLs/xzcuD6sZ1sDzTYihxbi19/RFiLYpWUAIfrDuRnJxssD05ORkBAQEAgICAAKSkpBg8r9FokJaWJr+eEGJZjAGCi2hwS0dkDAJ9ZSGE2AlFP46CgoIQGBiIo0ePytsyMzNx5swZNGnSBADQpEkTpKenIzo6Wt7n2LFjkCQJ4eHhSqZDCCkRg5pL0HAOfQ1FYAySbZNyCAUbnhjT3SKjfjuEKM/kcWhZWVm4deuW/DguLg4XLlyAt7c3qlWrhgEDBmDVqlWoVasWgoKCsGTJElSuXFke6RMSEoLWrVvj448/xvTp06FWqzFz5kx069bNpBE8hBBibQyAIDJIEsC0HMxZgIZzMJGBhhQSoiyTKyjR0dEYMGCA/Hju3LkAgJdffhnz5s3DkCFDkJOTgylTpiA9PR1PPvkk1q9fDxcXF/k1CxYswMyZMzFw4EAIgoBOnTrho48+UuDtEGJ72tp1kLrjG2hr13G4+NqQOkj99ltoQyyTuyPkIZddpw4Aw0qHrkMxhwQOUdC1Qt3JzkQ1T8Nhk1KdOri/ew+0deoUHrFdetkKnFtLX3+EWEu55kGxNZoHhZDS6TvCcm44D4qzlkEtct3tCQZomWPPT2LtcgrOhxKXmYYa3j6QOM2DQkhZbDYPCiEEEBLuotL8ORAS7to0vr4jrOgiQhAe9pJgAJgzA8SifSeEO3dRafZsCHcsk7uxbJmHXPZd88tmd+/Cfe4cOYaur4oRZStw7Vj6+iPEWqiCQojChIS7cF8wz6IVFGPi6+c3kURA6wx5xI7+NgUHR+EainD3LtznzC3XH2cl2DIPJcoWEu7CY54uxsMRU2KZlRQlrh1LX3+EWAtVUAh5hBWchK3giB1SfsYfSl1fFTWXwIxpRiGEAKAKCiEVgkbS2jqFR4p+NA8TqMJBiKVQBYUQQkxUcDQPNYoQYhlUQSFEYZK3D3JfeRWSt4/dxy/8t1Xy9UHua69B8i1/7PKwZR5y2T7ml819fJDzqukxlDi3lr7+CLEWGmZMyCNKEBgkZwZJ4LiZdh91PP103/ihG1osMQ4nQYDEAQ2XHonhv7Ysp6Rhxk4aBrVK90FFQ45JRUfDjAmxpdxcCNevAbm5dhNfZEzuM6FvNSl4m8Ig9jUL5m4sW+ahRNm5uRDNiaHEtWPp648QK6EKCiEKU12+CP9nmkB1+aLdxBcYgxZl95lQXbgI//AIqC5YJndj2TIPueyL5pctXrqIgKaNTY6hxLVj6euPEGuhCgohFQiNPiGEOAqT1+IhhDgu/W0dQgixd9SCQgghhBC7QxUUQgghhNgdusVDiMI04Y2ReC/dIeNrmjRGYlamRWI7Sh76siXOgYw0s2JoIxojIS0DAOCkMaFsBc6tpa8/QqyFWlAIIYQQYneogkKIwsSrV+DzfHuIV684XHzx8mX4tG0H8fJlxWM7Sh5KlC1cuQzfDu0gXjEthhLn1tLXHyHWQhUUQhTGsrPg9O9JsOwsh4vPsrLhdOIEWFa24rEdJQ+57Gzzy2bZ2XA+edLk/JU4t5a+/gixFqqgEEIIIcTuUAWFEEIUwgBa3ZgQhdAoHkIIUQADIKoEqqAQohBqQSFEYdoaNZG+Yi20NWo6XHxtrZpIX78e2lqWyd0R8pDLrmla2YwxqCUJGi4BwbWQtnYdpJo1wdjDBRrLLFuBc2vp648Qa2GcO+6810lJGXDc7AmxLEFgkJwZJIEjPiMNQV4+Je4r8dL3Ket5pfZx9HI451AJAgQJUGslCCoBXOKQGAAth6gGJIk+tEjFxRgQEOBp1L7UgkKIwlhSElw3rAVLSnK4+CwxEa5r1oAlJioe21HyKE/Z+lYU7b17cFu/Ftp7CYjPzgAHhzHNKEqcW0tff4RYC1VQCFGYGB8Hzw/egxgf53Dxxbjb8Hx3PMS424rHdpQ85LJvm1+2cPs2PMbrYmglrfFlK3BuLX39EWItVEEh5BHCGI0iIYQ8GmgUDyGPCMYAwUUEAEh5xn9rJ4QQe0QVFEIeEYwxqLkEAFBRMwohxMHRLR5CFMY9PJD/XDtwDw+bxze1msI9PZDfvj24p2Vyd4Q85LLLcf64h3kxlLh2LH39EWItNMyYkEeEIDBonHT/d9IAzFmAVgC0XLLbYbmPYjkS5xC5rmqoZbrnq3l6g0mAmE/DjEnFRsOMCbElrRYsIx3QWqgfiFHxGTSc64a3mho73YK5O0IeSpRtbgwlrh1LX3+EWAlVUAhRmOrcWQSEBEF17qzDxVdFnUVA1WpQRVkmd0fIQy77rPllq86ehX/14mOUNtJKiXNr6euPEGuhTrKEEKIgBkAo4asfYwBzfjjSim5RE1IyqqAQQoiCGGPQlljxMBxp5cBdAAmxOLrFQwghhBC7QxUUQgghhNgdusVDiMI0jzdA0vnr4N7eDhdf07ABkmJugPv4KB7bUfLQl6319gZys60aQ4lza+nrjxBroQoKIUpzcgIPCHDM+E5O4IGBlontKHnoy+YcyLVyDCXOraWvP0KshG7xEKIw4cZ1ePV/DcKN6w4XX7h+HV69X4Vw3TK5O0Ie+rLFcpRtbgwlzq2lrz9CrIUqKIQoTMhIh8svP0PISHe4+EJaOlx++glCmmVyd4Q89GWzdPPLLi4GQ9krTStxbi19/RFiLVRBIYQQC2MARJUA5sxMXh+JkIqKKiiEEGJhjDGoJQkazk1fwZGQCooqKIQQQgixO1RBIURh2seqIXP6HGgfq+Zw8bXVqiJz7lxoq1VVPLaj5KEvW6pq/vE1N4YS59bS1x8h1sK4A8+1nJSUQWtZEPKAIDBonHT/d9IwaJ0AiIDEOeIz0hDk5VPia8vaR4kYFb0ciXOInEHiHJwDKjUgSfQBRioWxoCAAE+j9qUWFEIUxlLvw3nvHrDU+xaNL6TdL3FUCGPmdXVg9+/D+ZtvwO5bJndHyEOJss2NocS1Y+nrjxBroQoKIQoTb92E9+CBEG/dtGh81Z1YCC5ikUoKA8CcGSCaXkURY27Cu/8AiDGWyd0R8pDLvml+2ebGUOLasfT1R4i1UAWFEAel4RxqLoEVqqEwxqDhHBx0+4AQ4riogkLII4RGsDoGYyZtI6SiowoKIY8AxgDBRQBEmgjM3j2ctE2gSgohpaDFAglRGHd1g7pRBLirm8XiaxpFgLu5Pvgmrvsrp+EcnPFyfTXnbq5QR+hi25It85DLdjW/7NJi6G7BSWAcEBlDwYGUSlw7lr7+CLEWGmZMiAMSBAbJCRBUArRaCU5MgJZzaAXdUFYA4A/aRx11WO6jVo7EOVScgQPQMg4mAWI+DTUmFQsNMybkEaa/ncNUAjRcQlxWOk2h7gAYAEFkYAKdKEKMQRUUQhSmOnsGAUEBUJ09o2hcxnQ/qrNR8PH3hXj6NABAI2kVK0N1+gwCfP2gOq1s7o6Uh1z2GfPLLi6GfnSVVMroKiWuHUtdf4RYG/VBIURpnIPl50PJ+4+6VhPRYvFlloztKHkoUbYRMYodyWOlsglxBNSCQogDYIxBzaVi5z0hjodG8hBSNmpBIYQQK2OMQS0VP5KHEKJDLSiEEGIHqCWFEEPUgkKIwjT1wpBy6Di0tYItEl8bGoaU4yegDamteGxN/TCknDwBbW3lYztKHvqyNcHBgCbfKjH0fYx4oydw//BxaGoGm1UuYPnrjxBroQoKIUpzc4O2/uOWjf/4E4AI5TtCurlB+8QTysZ0tDz0ZXMOZJhXQTE1hr6PEVxdoHr8CaA8c6NY+vojxEroFg8hChNib8HjnVEQYm9ZLv6okRBuPYyv1N0B4dYteLxtGNsWbJmHEmWbG0O4dQvuY0eW69qx9PVHiLVQBYUQhQn3U+D25RcQ7qdYJD5LSYHbF5shJOvii4wpNgGYkJwCt80PY9uKLfOQy04xv2xzYwgpKXAt57Vj6euPEGuhWzyEODiBMWgLTP7FgFKmAiOEEMdALSiEOJjSRnvQdOqEkEcFtaAQ4kAYAOZUyvMPplMnhBBHRxUUQhQmBVZG9ph3IQVWNvo1+laRsuoWjDGoAysje/x4SFWMj28sqYrlYjtKHnLZlc0v25QYunPPwMAhVa6MnLEPrx1jrwuDss24/gixR4wrPIXhsmXLsHz5coNttWvXxr59+wAAeXl5mDdvHn766Sfk5+cjMjISU6dORUBAgMllJSVl0HITxOEVXGdHytMWe00LAoPGSdffhDGAP7g5K3GO+Iw0BHn5lBhfiX2oHOXLkTiHIAEqJkDDue6cajlENSBJ3KjrghBHwxgQEOBp1L4W6YNSr149HDlyRP7Ztm2b/NycOXPw+++/Y/HixdiyZQvu3buHUaNGWSINQmyCZWbA6a/DYJkZxu1v4jo7LCMDTocOgWUYF98UloztKHkoUbaxMfTnPj47AxwcLDMDqiO6a8fc9ZdMvf4IsVcWqaCIoojAwED5x8/PDwCQkZGB3bt3Y9KkSWjRogUaNmyIOXPm4NSpUzj9YOl4QhydeP0afF7uBvH6NYvEF65dg8/zXSFeVT6+eNVysR0lD7nsa+aXbWoMraTVve7aNXj36Fqua8fS1x8h1mKRCsrNmzcRGRmJ9u3bY/z48YiPjwcAREdHQ61Wo2XLlvK+ISEhqFatGlVQSIXH8LDPAWO0NgshpGJTvJNseHg45s6di9q1ayMxMRErVqxA37598f333yMpKQlOTk7w8vIyeI2/vz8SExOVToUQh8EAiCoBTACEPC2YM/U9IIRUbIpXUNq0aSP/v379+oiIiEDbtm3x888/w9XVVeniCHkk6IYHS2Bc12lSzSUAgIoxKNyPnRBCHILFJ2rz8vJCcHAwbt26hYCAAKjVaqSnpxvsk5ycjMDAQEunQohVcJUTtFWrgatKmbCkPJxU0FarBu6k/CwB3IKxHSUPfdkox/kzNwZ3Kv+1Y/HrjxArsfhvf1ZWFmJjYxEYGIiGDRvCyckJR48eRefOnQEA169fR3x8PBo3bmzpVAixCu0TDZBy5qLl4jdoiJQrl3UPFG5d0TYsENuGbJmHvmyJcyAjzaoxtA0aIPXsJUgSN/vbo6WvP0KsRfEKyieffIK2bduiWrVquHfvHpYtWwZBEPDCCy/A09MTr7zyCubNmwdvb294eHhg1qxZaNKkCVVQSIWj6whrxLDiBx1mqc/so4fOKSElU/wWz927d/Huu++iS5cuGDduHHx8fLBr1y55qPHkyZPx3HPPYcyYMejXrx8CAgKwbNkypdMgxGbE8+fgF1Ef4vlzJe6jn4RL4wQILkKJf6gYA0QXEYKbCCYyXWfac9HwqxcKMTpa+dyjLRfbUfLQl62KLvn8KRGjuHWTxHPn4NMorNRrp8yyjbj+CHEEiregfPbZZ6U+7+LigqlTp2Lq1KlKF02IXWAaNcQ78WAadcn7PJiEKy4rHTU9vEsZUsygAQc4hwQOkTFArYEYHw+m1iifuwVjO0oe+rJRyvlTIoZ+3SQJ/OHwcvXDa8fcm3fGXH+EOAJazZgQG9I8mKDLWHRLgBBSUVAFhRAHId8SoFoKIaQCoAoKIQ5Cf0uAZkV5dFHdk5CHqIJCiMK0dUKQuudHaOuEWCZ+3RCk/vwTtHWVj2/J2I6Sh1x2iPllmxODAeBh9ZDx08+QCryu4BIIRpVt4euPEGux7WxMhDyCuIcn1K1aWy6+pyfUzz774IGy7SkGsW3Ilnnoy+blmAfFnBiMMajd3aGNjISK6b47CpIE4cESCCzXuGUPLH39EWIt1IJCiMKEO/FwnzUNwp34Yp8vbx8SIT4e7lOmQogvPr69xnaUPJQo29wYQnw83KdNg/Z2HDScgwm6JRDUXDJqzhyg7OuPEEdBFRRCFCYk3kOlpYsgJN4r8px+/pPS5j4pM37CPVRauBBCQtH45WXJ2I6Sh1z2PfPLNjeGcO8eKi0qZ9mlXH+EOBK6xUOIFennPxFAU8MSQkhpqAWFEDul6xzJqB5DCKmQqIJCiA2UVelgAESVAK0TB0SqolR0cmWVLgVSgVAFhRCFSb5+yOk7AJKvX7HPF7cGS5F9mK5zZHx2BgrPfCL5+yFn4EBI/sXHLw9LxnaUPOSy/cwv29wY3M8PeQMHggf4y5VYfWVV48QhuIhlVlLKuv4IcRSMc4XHKVpRUlKG0qMsCbEoQWCQnBkgAhLnuJl2H7U9/cAYoGUcggSoNAwaFSAJHPEZaQjy8ikxnsStsw+VY91yJM4hcl1NhAu6x7cz0lG9khdUakCS6IOPOCbGgIAAT6P2pRYUQpSWkwPx4gUgJ6fYp0tcuRi6b8rMuYym/JwciOfPlxi/XCwZ21HyUKJsc2OU8jr9uk2MlTFUvYzrjxBHQRUUQhSmunIJfs82h+rKJYPtuiHGQol9Shhj0EgSuIBSb/+oLl6C39PNoLp4qcR9zGXJ2I6Sh1z2JfPLNjdGWa9jAJizUOqtnpKuP0IcDQ0zJsRK9GvplHcfUnHph6lzDqgYgwPfoSekTNSCQgghdogG7JCKjioohBBiZ4wZ6UXIo44qKIQojTFwZ+fyL7pji/iWzt0R8lCibHNjPHgdYwI0nEMqNMS84NBjQSghvL2cQ0LKifqgEKIwTaMIJMUlWS5+4wgk3U/RPVC4D4JBbBuyZR76sqVyrGZsbozS3rfIGASRAVz3f+bEIGo4tHmGqxxb+vojxFqogkIIIQ5AYAxacIABGnBIkqQbmk6dZckjim7xEKIw8fIl+LRvDfHyw2Ge+rkrim2RNzX+xYvwadkK4sWL5crT2rEdJQ8lyjY3hiJlF3P9EeKIqAWFEIWx3Bw4nT0DlqubKIsxQHQRwVS6b7oFp67XN9tzoMiU9iXGz8mF05kzYDm5yuduwdiOkodcdq75ZZsbw9T3rVujp9C2QtcfIY6KKiiEWBhjDBrwYvuLyM32hJhInnmYASxXS8t+kEcOVVAIIcQBMcagliQwTv1QyKOJKiiEKIRGdRJCiHKogkKIAnTr7IgAAKlWLaSt3wxtzVoWKUsbXAtpW76ANlj5+JaM7Sh5yGXXMr9sc2Mo8b61NS17/RFiLVRBIUQBujV0dNNqqXz9kN/9ZYuVxX19kd+z54MHyjbrG8S2IVvmoS+bl2MeFHNjKPG+uY+vRa8/QqyFhhkTUk4FVylmANi9e3BbtRzs3j3LlJeQALely8ASEhwqtqPkoS9bSDD//JkbQ4n3XdL1xxggCIxuRRKHQRUUQspJvwIxfzCJlnAnHh5TJ0O8G2+R8sT4O/D44AOI8XccKraj5KEvW7hj/vkzN4YS71u8W/T609+C1Djp/qVKCnEEVEEhxAro7wGxpLJaRxhjUHMJcVnpUHMJjGooxAFQBYUQCxIEQHxw+4cQS2AAmLNgVOuIRtJaLS9CyosqKIRYkOAiQiMYP0ssIaYq2Dqi4RIEZ/pYJ48GGsVDiMK4lxfyOj8PeHtDY4HJsyRvL+R17QrJ28uhYjtKHvqyuZf5ZZsbozzvWyNpITAGtacn8p7XXX+EODKqoBDygL5p3Ng6RUlN6VLtOkjfshOCYJnbOlKdOkj/apfugcIVIIPYNmTLPPRlS+UYZmxujPK8b/26Ttq6IUjbsQuiGoBELXfEcVFbICF4OMrB2BEOBfcvQq0GS0oC1GrlE9XHT0y0THxLxnaUPJQo29wY5Shbv66TpM4HS7aDc0hIOVEFhRA8vI9f1ggHxvQ/Je8vnj+HgCfqQDx/ziK5qqLPISC4NlTRyse3ZGxHyUMu+5z5ZZsbw5z3XfhqVZ07h8A6lrv+CLEWusVDiJEKTmfP8yUbZ0OIrnIiiAwSXY7kEUQVFEKMpG81AQAnxgAamUNsTD9JoAROk6+RRw7d4iEVgv7WTHleX2ps/f/L2JcQQohxqAWFPPIMVhrO05o88EX/esYAJkkG7Sa6SbJ0NRTGOUSVAOZENRRif+iqJI6GKijkkVfw1oyKMd0Ks2a8XsCDphJu+JyGc3B9x1lJgqZhQ2hu3Aav5K7gu3hIE94ISXfiwd2Vj2/J2I6Sh75sbaVKQHamVWMo8b41jRohMe42BCddDP1illpGtySJY6EKCiGlKO52je42DgMrqQ+KKIJ7WnCCMVF8OAGY0hPBFYxtS7bMQ192eY6tuTGUeN/6GPkAJF6gEk39VIhjoT4ohBRQsA+J/taO6CI83AZAVAnQOnFAZMX+AolXr8Krdw+I169ZpFldvHoV3t1fgnj1qkPFdpQ8lCjb3BhKle3TowdUN65CoE944sDo8iXkAX3lgzkL8lwnGi5BEgAmMLnlRMMlxGdnAOAQVAys0IyxQmYmnH4/AJ6baZFFAllGJpwPHADLMO/2g61iO0oectmZ5pdtbgwl3reQmQnngwcg5GdDrKTSVbDNjkaI7VAFhVQIxnxA6ysfBSdfY/rZOcENgmglrcEQz8JxACAxN4sWCSRWxx5cqBrOkSdpoSl07QLlH9VGiDVQBYU80vQdBCEyq3yLZIDcrK6l2bOIHdKNPBOMXtaBEFuhCgp5pMkdBAt9izTnG6SxrTBa/rAMQmyt8GVo7LIOhNgaVVDII6/oB7RpCwPqYwhi0f4mxdEGVUf6woVQV6tmcq7G0AZVR8aihdAGVXeo2I6Sh1x2dfPLNjeGEu+7YAxTrltC7A0NMyaPLHn+BwEGwz3NmRdF3xJjDB4YiJxhQ6HJSDMrb2Pi5w4b9uCBsn1cDGLbkC3z0JfNOQfMPIfmxlDifReMwQCjr1tC7A21oJBHlsHtnVL3K266evO/cbKUFLju2AHx/n2zY5QV32X7DrCUFIeK7Sh5KFG2uTFsWXaJ8ahDLbERqqAQuyNXGMz8YGQMRs//IAiA6CJCcBPBHnSkFVUCNE7cYP4TU4g3b8F78BA4x8aZ/mIj43sNHgzx5i2Hiu0oechl3zK/bHNjKPG+jY2h79Bd1jpTpt4OJUQpVEEhduXh5Gi6H1M/GBl7UOEwYv4H/WgGrQioufRgRVjddPXx2Rny/CeEPGoYAJVKAHPV/Z6V9DtGHWqJLVEfFGJX5HVvGAMHB+em9BMBBEGAFhxaSYKIAvdtCuyjmymi4BTgRWNxLkFLc5iQR5R+fh+1JEEAIAoMWi1d78S+UAWF2CX9mnwFp54vss+DW0CcP6icOIvQgIOJrNjOo/oWEw3ngMAgSKAqCKlwWKH/iyrdrUwxXwtJKr3ftX6GZc654stAEVIY3eIhdkc/NFIQmK4Z2rloXxDDWzm6Zmq1wBGfnVFip1h960xp09QrgbtXQn6zpyFVqqR4bH18dbNm4O7Kx7dkbEfJQy67HOfP3BhKvO/SYoiMGQw7ZoxBI0mACAhuqjJu9+huv2qcQH1SiFVQCwqxO/rmZzBd3xDGdR+svNBQYflWzoNPSg4OiWvLjF9wmnpL0IaG4v7Bg8iz0DBjbWgoUn8/qHug8HswiG1DtsxDX7ZUjmHG5sZQ4n2XFkPQ/24VIC/ZwCVdi0qJt1R1Ffy4rHQEuXsZfeuVEHNRCwqxKWNG6hS+zaMbeSNYZCE+Qioy/RD74n4n9UPxNVLZXwIIUQJVUIjNGDOEsegKww+amQXY7UJ8qlOnUcXDE25RZy0WP9DdA6pTpx0qtqPkIZd92vyyzY2hxPs2N0bBIfaFfyd1/bcYfSkgVkUVFGIz+j4hGi5BfNDnpOCEafI+0sNhjsVNvlbSR2aRKe6VfgOEPEL0v2txWeny72TB5/S/d8X1BytrLhXqr0LMQX1QiNn0HzqFb0MX3F7cPoW/malUApgTg6TWwslJ1I0QKHyfHLpbO5wzeYSPvF1kkLhhIQW3c/CHj2mBYUJKxbkk/06KWl7qqtz6Fk0AkPK0xX4WlPY8IaWhCgoxS0kfPAW383wtmLPhPobPSwbzMSTkZqKqyrNoWXhQiVExaDXSg2Zm/qC84ju7Ft4udwQs5hsgIeQhocDvZHFzCRVU1rpW5qx7RYge3eJxMEo1lxobRzf52cNOcw+noGfQFJphUr9df9tGEASDWSgLPl/czJTaEjrf6SsXeZIW8TklDyMmhCjLmI8ahtKnzafvA8Rc1ILiQEprtdBPWGbMF5Ti4hSusBSc/CyfS3Biotwioh9Vo2UcTMsNYjIGCJIEQSWAiQCT+MP4zrrnmSSZXcUoqRJjTzSP10fSmdPI9fKwWPzkqDOQqld3qNiOkoe+bE21aoA6z6oxlHjfSh27grdFi5vUUBAA5iRAYhwqxnS3hDQcWoPWUsHgc8Losku5NUyNMBUHVVAcSHHNpfoJy6BiQIEPB1PiAFyOoa+jcI0udj7jiEvXzXvgxAR5Gnrdq3iBlpUHU9RDNwmU9kELCgMDtLo5YfXPG3QieRS5ukIbEgJuoXlQ4OoKKSRE93+lP60LxrYlW+ahL5tzsysoZsdQ4n0rdOwKdowVVIJBPy95VuYHv/8acEiS4TwqD5eSMO22aklfoKgvS8VDt3isxNye7sVtLzgvCGMMGnDkS1qoUfyCXg9vy5RUhu6es/6WTZ6khZZxaHSr4UDLtaVOOV84NzmvAn0+2INKDSuw36PaF0SIiYHXoMFwLsdquGXF9/zfIAgxMQ4V21HyUKJsc2PYsuySFPxdLrytpM7sQqEZmku6BVTwM6ng7ePCt4Fp0cKKyaYVlC+//BLt2rVDo0aN0Lt3b0RFRdkyHbPo+miUXfnQr9ArFHPES5oPRDchmeH2wvOCGMTBw1wK/sirA/+/vXOPafJ64/i3b6nTzf2IN+IWNVswReR+MSjiLm7AoszJ5m2b25jM27zOxegWdSM6UYNu4m0oSsZ0QUd1yQSNl0TdAm7OAA5hYKOCOt24ChUYlD6/P0pf+tICbSlt1eeTNGnP+7znPM/79Jz3Oec957xm3lxqGIbtuL+B8S+5TNZWpjTIMM5DJpNBACTbaBvr26rQvyNHLtnC3vyGUA87Qk0t+h0+DHlt74ygCDW16Hv4MISa2ocq74dFD7HsWtvLtjUPe9jtrGsnTmbvK0erQr+ZomHbAOM3Jxu3UYZ2TxDavxvn19mmcZJyzQQ5zKOB0wKU7OxsJCYmYtGiRTh27BhGjRqF+Ph4VFVVOUslqzE8XjG8D6azimGYUKqTm3+Hhfkeg162RdCPbBj3JDpOTgVMG4H2wEYvT3L9nBGS6wMS4zPN7StiHGgIbXlo2x4pSd7lAf1Qb6sCkLnp3yTcsafVomt7/41MPxRMMuh1EtAr78JhGMbxdJzMrmur38bpWuiMOmPG7V6HCfWQbhrXeZnmgxwOUh4NnBagpKWlYcaMGXjrrbcwcuRIJCQkoG/fvlCpVM5SySyGERLzw5P6RyOGxyuCIBOHNw3nGM5rv8m3zc2Qtedt6GW0D48CgiCg1WhjJGM5QNq7MHw3NAKtMkIL2ssyLBskmf7dNq1EXU6tNzek29FmwzHjF/B1tbqm4+TWrspgGMa16e7+T6Qz6awA+nbQpDMGoxEQtI/Iatve+2PSGZOMmJgPcoxXHhrO6W6km3E9nDJJtrm5GVevXsX8+fPFNEEQEB4ejry8PIvz6a0/m6QCKNoff1ArAS0EcQ+OPgJ0AulfZtcmK3OTobWlFXI3t3Y56CeUCtT2uAM6uEGuPy7X9xYE0kGQCYCbDIIM0Gl1kMllEIggl+t1ENrmhBjy0UEHN7kchPaNlGRojzoFtzb9BJmoiwB9r0YgAK3U9mim89BCBkAhdxPzpQ7HDGmGibPd5dHpNbeDjMuUI5cDTz8NhULRO+W05Q+53O42d8zbljzsYXNneti7HLPH28qWyeW2l2NrHjZef3vkYWk5fd0UENraRLOdmE7yMNf2yNsm4MoEQE4EQa7/bmiThLZHx+L50LezAgho0QHQt1/oRkamkAFyWVsbbm7jufY2snPLu+tM2UPGtcohIrtPSLZqwjQ5Yeecf/75By+88AIyMjIQFBQkpm/ZsgWXLl3Cjz/+6GiVGIZhGIZxIXgVD8MwDMMwLodTApQBAwZALpebTIitqqrC4MGDnaESwzAMwzAuhFMClD59+sDHxwe5ublimk6nQ25uruSRD8MwDMMwjydO20n2ww8/xKpVq+Dr6wt/f3989913aGxsxJtvvukslRiGYRiGcRGcFqBMmjQJ1dXVSE5ORkVFBby9vZGamsqPeBiGYRiGcc4qHoZhGIZhmK7gVTwMwzAMw7gcHKAwDMMwDONycIDCMAzDMIzLwQEKwzAMwzAux2MXoOzZswezZs1CQEAAQkNDLTqHiLB9+3ZERETA398fcXFxuHnzpkSmtrYWn376KYKDgxEaGorPP/8cDx486AULusZaPW7fvg0vLy+znxMnTohy5o5nZWU5wiQTbLnW7733non+69atk8j8/fffmDdvHgICAjBu3Dhs3rwZWq22N00xi7X21dbWYv369YiOjoa/vz9eeuklbNiwAfX19RI5Z/rw0KFDmDhxIvz8/DB9+nRcuXKlS/kTJ07gtddeg5+fH15//XWcP39ectySOulIrLHvyJEjeOeddzBmzBiMGTMGcXFxJvKrV6828VV8fHxvm9Ep1th39OhRE939/PwkMq7mP8A6G821J15eXpg3b54o40o+vHTpEhYsWICIiAh4eXnhzJkz3Z7z22+/ITY2Fr6+voiMjMTRo0dNZKyt11ZDjxnbt2+ntLQ0SkxMpJCQEIvOSUlJoZCQEDp9+jQVFxfTggULaOLEidTU1CTKxMfH05QpUyg/P58uXbpEkZGRtGLFit4yo1Os1UOr1dK///4r+ezYsYMCAwNJo9GIckqlklQqlUTO2H5HYsu1nj17Nq1Zs0aif319vXhcq9VSTEwMxcXFUVFREZ07d47CwsJo69atvW2OCdbaV1JSQosXL6azZ89SWVkZ5eTkUFRUFC1ZskQi5ywfZmVlkY+PD2VmZtK1a9dozZo1FBoaSpWVlWblL1++TN7e3rRv3z5Sq9X09ddfk4+PD5WUlIgyltRJR2GtfStWrKCDBw9SUVERqdVqWr16NYWEhNC9e/dEmVWrVlF8fLzEV7W1tY4ySYK19qlUKgoODpboXlFRIZFxJf8RWW9jTU2NxL7S0lLy9vYmlUolyriSD8+dO0fbtm2jU6dOkVKppNOnT3cpX15eTgEBAZSYmEhqtZq+//578vb2pgsXLogy1l4zW3jsAhQDKpXKogBFp9PR+PHjKTU1VUyrq6sjX19fOn78OBERqdVqUiqVdOXKFVHm/Pnz5OXlJWl0eht76fHGG2/QZ599Jkmz5E/tCGy1cfbs2bRhw4ZOj587d45GjRolaUh/+OEHCg4Opv/++88+yluAvXyYnZ1NPj4+1NLSIqY5y4fTpk2jhIQE8XdraytFRERQSkqKWflly5bRvHnzJGnTp0+ntWvXEpFlddKRWGtfR7RaLQUFBdGxY8fEtFWrVtHChQvtrapNWGtfd22rq/mPqOc+TEtLo6CgIHrw4IGY5ko+NMaSdmDLli00efJkSdry5ctpzpw54u+eXjNLeOwe8VjL7du3UVFRgfDwcDHt6aefRkBAAPLy8gAAeXl5+N///icZxgwPD4cgCPYf8uoCe+hRWFiI4uJiTJs2zeRYQkICwsLCMG3aNGRmZoKcsIVOT2z8+eefERYWhpiYGGzduhWNjY3isfz8fCiVSslGgREREdBoNFCr1fY3pBPs9V/SaDTo378/3NykezE62ofNzc24evWqpP4IgoDw8HCx/nQkPz8f48aNk6RFREQgPz8fgGV10lHYYl9HGhsbodVq4e7uLkn//fffMW7cOERHR+OLL75ATU2NXXW3BFvta2howMsvv4wXX3wRCxcuxLVr18RjruQ/wD4+VKlUmDx5Mp588klJuiv40Ba6q4P2uGaW4LSdZB8WKioqAACDBg2SpA8aNAiVlZUAgMrKSgwcOFBy3M3NDe7u7uL5jsAeemRmZsLT0xPBwcGS9KVLl2Ls2LHo168ffv31VyQkJKChoQHvv/++3fS3BFttjImJwbPPPgsPDw+UlJQgKSkJN27cwM6dO8V8O+5ibPj9sPmwuroau3fvxsyZMyXpzvBhTU0NWltbzdaf69evmz3HnC+M65slddJR2GJfR5KSkuDh4SFp7CdMmIDIyEgMGzYMt27dwrZt2zB37lwcPnwYcrncrjZ0hS32Pf/889i4cSO8vLxQX1+PAwcOYNasWcjKysLQoUNdyn9Az3145coVlJaW4quvvpKku4oPbaGz9lCj0aCpqQn379/v8f/eEh6JACUpKQn79u3rUiY7Oxuenp4O0si+WGpfT2lqasLx48fx8ccfmxxbtGiR+H306NFobGzE/v377XZz620bjW/WXl5eGDJkCOLi4lBeXo4RI0bYnK+lOMqHGo0G8+fPh6enJxYvXiw51ts+ZKxn7969yM7ORnp6Op544gkxffLkyeJ3wwTLV199VeyRuzJBQUGSl74GBQVh0qRJyMjIwPLly52nWC+RmZkJpVIJf39/SfrD7ENX4ZEIUObMmYPY2NguZYYPH25T3kOGDAEAVFVVwcPDQ0yvqqrCqFGjAOgjy+rqasl5Wq0W9+/fF8/vCZba11M9Tp48iaamJkydOrVb2YCAAOzevRvNzc3o06dPt/Ld4SgbDQQEBAAAysrKMGLECAwePNjkEYqhN/ew+FCj0eCjjz7CU089hV27dkGhUHQpb28fmmPAgAGQy+WoqqqSpFdVVXX63q3Bgweb9KSN5S2pk47CFvsM7N+/H3v37kVaWlq3eg8fPhwDBgxAWVmZQ29uPbHPgEKhgLe3N8rLywG4lv+AntnY0NCArKwsLF26tNtynOVDWzBXBysrK9G/f3/07dsXgiD0+H9hCY/EHJSBAwfC09Ozy4+tDfCwYcMwZMgQ5ObmimkajQYFBQViLyEoKAh1dXUoLCwUZS5evAidTmcSVduCpfb1VA+VSoWJEyeaPGIwR3FxMdzd3e12Y3OUjcb6A+2NZWBgIEpLSyUVLicnB/3798fIkSNd3j6NRoP4+HgoFArs2bNH0hvvDHv70Bx9+vSBj4+PpP7odDrk5uZKetnGBAYG4uLFi5K0nJwcBAYGArCsTjoKW+wDgH379mH37t1ITU01WYJrjnv37qG2ttYuwbI12GqfMa2trSgtLRV1dyX/AT2z8eTJk2hubsaUKVO6LcdZPrSF7uqgPf4XFmG36bYPCXfu3KGioiJxKW1RUREVFRVJltRGR0fTqVOnxN8pKSkUGhpKZ86cob/++osWLlxodpnx1KlTqaCggP744w+Kiopy2jLjrvS4d+8eRUdHU0FBgeS8mzdvkpeXF50/f94kz7Nnz9KRI0eopKSEbt68SYcOHaKAgADavn17r9tjDmttLCsro507d9Kff/5Jt27dojNnztArr7xC7777rniOYZnxnDlzqLi4mC5cuEBjx4512jJja+yrr6+n6dOnU0xMDJWVlUmWNWq1WiJyrg+zsrLI19eXjh49Smq1mtauXUuhoaHiiqmVK1dSUlKSKH/58mUaPXo07d+/n9RqNSUnJ5tdZtxdnXQU1tqXkpJCPj4+dPLkSYmvDG2QRqOhTZs2UV5eHt26dYtycnIoNjaWoqKiHLqizFb7duzYQb/88guVl5dTYWEhffLJJ+Tn50fXrl0TZVzJf0TW22jg7bffpuXLl5uku5oPNRqNeK9TKpWUlpZGRUVFdOfOHSIiSkpKopUrV4ryhmXGmzdvJrVaTQcPHjS7zLira2YPHolHPNaQnJyMY8eOib8NjzPS09MRFhYGALhx44Zkk6u5c+eisbER69atQ11dHUJCQpCamirppSYlJWH9+vX44IMPIAgCoqKisGbNGscYZUR3erS0tODGjRuSFSyAfvRk6NChiIiIMMnTzc0Nhw4dwsaNGwEAI0aMwOrVqzFjxozeNaYTrLVRoVAgNzcX6enpaGhowDPPPIOoqCjJXBu5XI5vv/0WX375JWbOnIl+/fohNjbWoqFbZ9t39epVFBQUAAAiIyMleZ09exbDhg1zqg8nTZqE6upqJCcno6KiAt7e3khNTRWHgu/evQtBaB/MDQ4ORlJSEr755hts27YNzz33HHbt2gWlUinKWFInHYW19mVkZKClpcXkv7V48WIsWbIEcrkcpaWl+Omnn1BfXw8PDw+MHz8ey5Yt69XRrs6w1r66ujqsXbsWFRUVcHd3h4+PDzIyMiQjka7kP8B6GwHg+vXruHz5Mg4cOGCSn6v5sLCwUDLXLDExEQAQGxuLTZs2oaKiAnfv3hWPDx8+HCkpKUhMTER6ejqGDh2KDRs2YMKECaJMd9fMHsiInLBWlGEYhmEYpgseiTkoDMMwDMM8WnCAwjAMwzCMy8EBCsMwDMMwLgcHKAzDMAzDuBwcoDAMwzAM43JwgMIwDMMwjMvBAQrDMAzDMC4HBygMwzAMw7gcHKAwDMMwDONycIDCMAzDMIzLwQEKwzAMwzAuBwcoDMMwDMO4HP8H7QUfGUKqZUEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "# take the relative centroid diff\n",
    "df[\"spectral_centroid_diff\"] = df[\"spectral_centroid\"].diff() / df[\"spectral_centroid\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95, 98]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Spectral Centroid Difference ratio --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:07.199140Z",
     "iopub.status.busy": "2025-06-01T12:50:07.198795Z",
     "iopub.status.idle": "2025-06-01T12:50:07.300271Z",
     "shell.execute_reply": "2025-06-01T12:50:07.299703Z",
     "shell.execute_reply.started": "2025-06-01T12:50:07.199123Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    34010\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    17005\n",
      "True     17005\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-v4-up-u-d-2    34010\n",
      "Name: count, dtype: int64 preference  model_name       \n",
      "False       chirp-v4-up-u-d-2    17005\n",
      "True        chirp-v4-up-u-d-2    17005\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    34010\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row[\"continue_at\"]\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id], row[\"continue_at\"]\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:07.300984Z",
     "iopub.status.busy": "2025-06-01T12:50:07.300836Z",
     "iopub.status.idle": "2025-06-01T12:50:07.608779Z",
     "shell.execute_reply": "2025-06-01T12:50:07.608274Z",
     "shell.execute_reply.started": "2025-06-01T12:50:07.300969Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n",
      "2.0\n",
      "5.566666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"total_shimmer_score\"].dropna(), [50, 75, 90]\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    print(percentile)\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(f\"Shimmer score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:07.609480Z",
     "iopub.status.busy": "2025-06-01T12:50:07.609330Z",
     "iopub.status.idle": "2025-06-01T12:50:08.368940Z",
     "shell.execute_reply": "2025-06-01T12:50:08.368355Z",
     "shell.execute_reply.started": "2025-06-01T12:50:07.609465Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1316 duplicated prompts 658 unique requests\n",
      "Found 323 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['3ee6de45-c62f-4b8f-971b-678d68587f4e', '7dc737bd-64ff-4cea-bf21-8ab041d473ad', 'c749eccb-d305-4e5e-bb0f-7ca71d9784b3', '2a7a7c72-5319-4486-bfb2-b91984ba19ff', 'f97c28d3-0db8-4c2a-ba09-ed534df2d37f', '09e02160-9411-42d2-8ead-a0a4460dc413', '1a776f0d-bf53-4966-be09-d005c9b4b4a4', 'ea43ceed-0b31-4c52-b122-89c5e437d731', 'f22399cf-341d-4f60-adc1-e8d53006914b', '512fc223-3907-4243-989e-938836d44843']\n",
      "Before dedup user gen requests 34010\n",
      "After dedup user gen requests 33364\n"
     ]
    }
   ],
   "source": [
    "# Find duplicated prompts with count > 2\n",
    "df[\"tags\"] = df[\"metadata\"].apply(lambda x: x.get(\"tags\", \"\"))\n",
    "duplicate_entries = df.groupby([\"user_id\", \"prompt_text\", \"tags\"]).filter(\n",
    "    lambda x: len(x) > 2\n",
    ")\n",
    "print(\n",
    "    \"Found\",\n",
    "    len(duplicate_entries),\n",
    "    \"duplicated prompts\",\n",
    "    len(duplicate_entries[\"request_id\"].unique()),\n",
    "    \"unique requests\",\n",
    ")\n",
    "\n",
    "# Group by user_id, prompt_text, and tags to find duplicate prompt groups\n",
    "prompt_groups = duplicate_entries.groupby([\"user_id\", \"prompt_text\", \"tags\"])\n",
    "\n",
    "# For each prompt group, find the request_id with the highest total reaction_play_count\n",
    "low_play_count_request_ids = []\n",
    "for prompt_key, prompt_group in prompt_groups:\n",
    "    # Get the sum of reaction_play_count for each request_id in this group\n",
    "    request_play_counts = prompt_group.groupby(\"request_id\")[\n",
    "        \"reaction_play_count\"\n",
    "    ].sum()\n",
    "\n",
    "    # Find the max play count in this group\n",
    "    max_play_count = request_play_counts.max()\n",
    "\n",
    "    # Add request_ids that don't have the max play count to our filter list\n",
    "    lower_play_count_request_ids = request_play_counts[\n",
    "        request_play_counts < max_play_count\n",
    "    ].index.tolist()\n",
    "    low_play_count_request_ids.extend(lower_play_count_request_ids)\n",
    "\n",
    "# Display the filtered request IDs\n",
    "print(\n",
    "    f\"Found {len(low_play_count_request_ids)} request_ids with duplicate prompts but not highest play counts in their group\"\n",
    ")\n",
    "print(\n",
    "    low_play_count_request_ids[:10]\n",
    "    if len(low_play_count_request_ids) > 10\n",
    "    else low_play_count_request_ids\n",
    ")\n",
    "print(\"Before dedup user gen requests\", df.shape[0])\n",
    "df = df[~df[\"request_id\"].isin(low_play_count_request_ids)]\n",
    "print(\"After dedup user gen requests\", df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:08.369653Z",
     "iopub.status.busy": "2025-06-01T12:50:08.369507Z",
     "iopub.status.idle": "2025-06-01T12:50:08.418711Z",
     "shell.execute_reply": "2025-06-01T12:50:08.418191Z",
     "shell.execute_reply.started": "2025-06-01T12:50:08.369637Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 15067 positive 6807\n",
      "total pair requests 16682 selected pair requests 6095 frac 0.365\n"
     ]
    }
   ],
   "source": [
    "normal_pos_play_count = 3\n",
    "# this is lower, cause a concat is probably already ensuring that it is good\n",
    "concat_pos_play_count = 2\n",
    "# this is a filter on the concated clip\n",
    "concat_total_play_count = 3\n",
    "\n",
    "neg_filter_selection_mask = (\n",
    "    (~df[\"preference\"])  # get basics aligned\n",
    "    & (df[\"reaction_play_count\"] >= 1)  # has to be played once\n",
    "    # & (df[\"play_count\"] <= 3)  # if it is actually bad, shouldn't be listened often\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    # & (df[\"duration\"] <= 60)  # can't be badly long\n",
    "    & (df[\"has_continue_and_start_continue_at\"].isna())  # won't have any continues\n",
    "    & (df[\"norm_play_frac\"] <= 2.1)\n",
    "    # & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "    # & (df[\"dislike_count\"] >= 1) # this is kinda strict\n",
    "    #     & (\n",
    "    #         (df_slice[\"is_in_playlist\"] == False)\n",
    "    #         & (df_slice[\"concat_in_playlist\"] == False)\n",
    "    #     )  # can't be part of a playlist -- otherwise there are some like signal in it?\n",
    ")\n",
    "pos_filter_selectin_mask = (\n",
    "    (df[\"preference\"])  # get basics aligned\n",
    "    & (\n",
    "        df[\"good_continue_at\"]\n",
    "    )  # if continue, needs to continue off a certain percentage\n",
    "    & (df[\"reaction_play_count\"] >= 1)\n",
    "    & (df[\"play_rel_diff\"] >= 0)  # this is more like quality assurance\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    & (df[\"dislike_count\"] == 0)  # can't have dislikes\n",
    "    & (df[\"flag_count\"] == 0)  # can't have issues\n",
    "    & (\n",
    "        (\n",
    "            (df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= concat_pos_play_count)\n",
    "            & (df[\"concat_play_counts\"] >= concat_total_play_count)\n",
    "        )\n",
    "        | (\n",
    "            (~df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= normal_pos_play_count)\n",
    "            & (\n",
    "                df[\"norm_play_frac\"] >= 2.1\n",
    "            )  # this is a bit of a luxury cut...not for now...\n",
    "            & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "        )\n",
    "    )\n",
    "    & (abs(df[\"mean_ear_score_diff\"]) >= 1)\n",
    "    & (abs(df[\"mean_ear_score_diff_ratio\"]) >= 0.05)\n",
    "    # & (df[\"norm_play_frac\"] >= 1.9)\n",
    "    # & (df[\"user_n_clips\"] >= 100)  # user needs to have genereated at least 20\n",
    "    # & (df[\"duration_rel_diff\"] < 10) # positive isn't just longer\n",
    "    # & ((df[\"task\"] == \"\") | (df[\"task\"] == \"extend\"))\n",
    "    # & (\n",
    "    #     (df[\"upvote_count\"] >= 1)\n",
    "    #     | (df[\"reaction_play_count\"] >= 5)\n",
    "    #     | (df[\"concat_play_counts\"] >= 5)\n",
    "    # )\n",
    "    # & (df[\"pos_diff_preference\"] == 2)\n",
    "    # & ((0 < df[\"similarity\"]) &  (df[\"similarity\"] <= 0.99))\n",
    "    # & (\n",
    "    #     (df[\"cer_diff_preference\"] < 0.25) & (df[\"cer\"] < 0.8)\n",
    "    # )  # cut on hoot cer difference and abs cer\n",
    "    # & (df[\"pair_quality\"] > 0.31)  # bottom 5%\n",
    "    # & ((df[\"total_shimmer_score\"] < 1) | (df[\"shimmer_score_diff\"] < 0.4))\n",
    "    # & (df[\"stereo_width_diff\"] > -0.2)  # cut off bottom 5%\n",
    "    # & (df[\"spectral_centroid_diff\"] < 0.25)  # crop off the top 5%\n",
    ")\n",
    "print(\n",
    "    \"negative\",\n",
    "    sum(neg_filter_selection_mask),\n",
    "    \"positive\",\n",
    "    sum(pos_filter_selectin_mask),\n",
    ")\n",
    "\n",
    "neg_filter_requests = df[neg_filter_selection_mask][\"request_id\"].unique()\n",
    "pos_filter_requests = df[pos_filter_selectin_mask][\"request_id\"].unique()\n",
    "# looking for very strong signal here:\n",
    "# listen to the positive/negative more than once\n",
    "# disliked one of the clips\n",
    "unique_requests = set(pos_filter_requests).intersection(neg_filter_requests)\n",
    "print(\n",
    "    \"total pair requests\",\n",
    "    df[\"request_id\"].nunique(),\n",
    "    \"selected pair requests\",\n",
    "    len(unique_requests),\n",
    "    f\"frac {len(unique_requests) / df['request_id'].nunique():.3f}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:08.419483Z",
     "iopub.status.busy": "2025-06-01T12:50:08.419335Z",
     "iopub.status.idle": "2025-06-01T12:50:08.466541Z",
     "shell.execute_reply": "2025-06-01T12:50:08.466016Z",
     "shell.execute_reply.started": "2025-06-01T12:50:08.419468Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 6095 clips 12190 total khrs 0.632; N gpus for 1000 iters 0.762; 4 gpus for x iters 190.469; n unique users 5213 n pro users 3812\n"
     ]
    }
   ],
   "source": [
    "df_slice = df[df[\"request_id\"].isin(set(unique_requests))].copy()\n",
    "\n",
    "#  requests 8723 clips 17446 total khrs 0.911; N gpus for 1000 iters 1.090; 4 gpus for x iters 272.594; n unique users 7300 n pro users 5452print(\n",
    "print(\n",
    "    f\"{os.path.basename(OUT_DATA_DIR)} requests\",\n",
    "    df_slice[\"request_id\"].nunique(),\n",
    "    \"clips\",\n",
    "    df_slice.shape[0],\n",
    "    f\"total khrs {sum(df_slice['duration'] / 3600 / 1000):.3f};\",\n",
    "    f\"N gpus for 1000 iters {df_slice.shape[0] / 8 / 2 / 1000:.3f};\",\n",
    "    f\"4 gpus for x iters {df_slice.shape[0] / 8 / 2 / 4:.3f};\",\n",
    "    f\"n unique users {df_slice['user_id'].nunique()}\",\n",
    "    f\"n pro users {df_slice[df_slice['is_pro_user']]['user_id'].nunique()}\",\n",
    ")\n",
    "# up t7 requests 37735 clips 75470 total khrs 3.964; N gpus for 1000 iters 4.717; 4 gpus for x iters 1179.219; n unique users 19093 n pro users 16217\n",
    "# up t17 requests 49262 clips 98524 total khrs 5.187; N gpus for 1000 iters 6.158; 4 gpus for x iters 1539.438; n unique users 24093 n pro users 20326\n",
    "# up v2 t2 requests 10772 clips 21544 total khrs 1.154; N gpus for 1000 iters 1.347; 4 gpus for x iters 336.625; n unique users 6527 n pro users 6027\n",
    "# up v3 t10 requests 27102 clips 54204 total khrs 2.938; N gpus for 1000 iters 3.388; 4 gpus for x iters 846.938; n unique users 13932 n pro users 12187\n",
    "# up v4 t1  requests 3201 clips 6402 total khrs 0.343; N gpus for 1000 iters 0.400; 4 gpus for x iters 100.031; n unique users 2363 n pro users 2321\n",
    "# up v4 t7  requests 31797 clips 63594 total khrs 3.384; N gpus for 1000 iters 3.975; 4 gpus for x iters 993.656; n unique users 16398 n pro users 15325\n",
    "# up v5 t2  requests 20030 clips 40060 total khrs 2.108; N gpus for 1000 iters 2.504; 4 gpus for x iters 625.938; n unique users 12395 n pro users 9156\n",
    "# up v6 t11  requests 43842 clips 87684 total khrs 4.588; N gpus for 1000 iters 5.480; 4 gpus for x iters 1370.062; n unique users 24777 n pro users 16668\n",
    "# v2 v1 t0  requests 3688 clips 7376 total khrs 0.381; N gpus for 1000 iters 0.461; 4 gpus for x iters 115.250; n unique users 3261 n pro users 2141\n",
    "# v2 v1 t1-5  requests 5406 clips 10812 total khrs 0.565; N gpus for 1000 iters 0.676; 4 gpus for x iters 168.938; n unique users 4655 n pro users 3180\n",
    "# v2 v1 t1-6   requests 9134 clips 18268 total khrs 0.952; N gpus for 1000 iters 1.142; 4 gpus for x iters 285.438; n unique users 7658 n pro users 5242\n",
    "# v2 v1 t1-7  requests 12274 clips 24548 total khrs 1.282; N gpus for 1000 iters 1.534; 4 gpus for x iters 383.562; n unique users 10036 n pro users 6818\n",
    "# v2 v1 t1-17  requests 14235 clips 28470 total khrs 1.497; N gpus for 1000 iters 1.779; 4 gpus for x iters 444.844; n unique users 11018 n pro users 8181\n",
    "# v2 v1 t1-18  requests 10007 clips 20014 total khrs 1.044; N gpus for 1000 iters 1.251; 4 gpus for x iters 312.719; n unique users 7998 n pro users 5831\n",
    "# v2 v1 t1-19  requests 6095 clips 12190 total khrs 0.632; N gpus for 1000 iters 0.762; 4 gpus for x iters 190.469; n unique users 5213 n pro users 3812\n",
    "# v2 v1 t1-20  requests 8723 clips 17446 total khrs 0.911; N gpus for 1000 iters 1.090; 4 gpus for x iters 272.594; n unique users 7300 n pro users 5452"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:08.467306Z",
     "iopub.status.busy": "2025-06-01T12:50:08.467159Z",
     "iopub.status.idle": "2025-06-01T12:50:08.484498Z",
     "shell.execute_reply": "2025-06-01T12:50:08.484019Z",
     "shell.execute_reply.started": "2025-06-01T12:50:08.467291Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (1533, 119)\n"
     ]
    }
   ],
   "source": [
    "test_mask = (df_slice[\"preference\"]) & (\n",
    "    (df_slice[\"is_in_playlist\"]) | (df_slice[\"concat_in_playlist\"])\n",
    ")\n",
    "print(\"positive in playlist\", df_slice[test_mask].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:08.485129Z",
     "iopub.status.busy": "2025-06-01T12:50:08.484987Z",
     "iopub.status.idle": "2025-06-01T12:50:08.498424Z",
     "shell.execute_reply": "2025-06-01T12:50:08.497994Z",
     "shell.execute_reply.started": "2025-06-01T12:50:08.485114Z"
    }
   },
   "outputs": [],
   "source": [
    "# def modify_model_name(model_name, metadata):\n",
    "#     if (\n",
    "#         model_name.startswith(\"chirp-v3p5-engine-t\")\n",
    "#         or model_name.startswith(\"chirp-v3p5-engine-s\")\n",
    "#         or model_name.startswith(\"chirp-v4\")\n",
    "#         or model_name.startswith(\"chirp-v3p5-h-s-31\")\n",
    "#     ):\n",
    "#         if \"param_experiment\" in metadata:\n",
    "#             exp = metadata.get(\"param_experiment\", \"\")\n",
    "#             if exp:\n",
    "#                 return f\"{model_name}_{exp}\"\n",
    "#     return model_name\n",
    "\n",
    "# metrics_check_df_slice = df_slice.copy()\n",
    "# metrics_check_df_slice[\"model_name\"] = metrics_check_df_slice.apply(\n",
    "#     lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n",
    "# )\n",
    "# get_preference_counts(metrics_check_df_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:08.499013Z",
     "iopub.status.busy": "2025-06-01T12:50:08.498881Z",
     "iopub.status.idle": "2025-06-01T12:50:08.514413Z",
     "shell.execute_reply": "2025-06-01T12:50:08.513959Z",
     "shell.execute_reply.started": "2025-06-01T12:50:08.498999Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web        6258\n",
      "android    3534\n",
      "ios        2398\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:50:08.515000Z",
     "iopub.status.busy": "2025-06-01T12:50:08.514866Z",
     "iopub.status.idle": "2025-06-01T12:50:09.026975Z",
     "shell.execute_reply": "2025-06-01T12:50:09.026343Z",
     "shell.execute_reply.started": "2025-06-01T12:50:08.514987Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'BREAK' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m df_slice\u001b[38;5;241m.\u001b[39mto_pickle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/home/tony/Data/Preference/up_diff2_v1/interesting_clips_upv2_u1_20250417_full_post_cut.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mBREAK\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BREAK' is not defined"
     ]
    }
   ],
   "source": [
    "# df_slice.to_pickle(\"/home/tony/Data/Preference/up_diff2_v1/interesting_clips_upv2_u1_20250417_full_post_cut.pkl\")\n",
    "BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need to kick out the ones has gpt prompt -- these are pairs with different text inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:33.565057Z",
     "iopub.status.busy": "2025-06-01T12:51:33.564702Z",
     "iopub.status.idle": "2025-06-01T12:51:34.447552Z",
     "shell.execute_reply": "2025-06-01T12:51:34.446903Z",
     "shell.execute_reply.started": "2025-06-01T12:51:33.565037Z"
    }
   },
   "outputs": [],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:34.448938Z",
     "iopub.status.busy": "2025-06-01T12:51:34.448505Z",
     "iopub.status.idle": "2025-06-01T12:51:34.499511Z",
     "shell.execute_reply": "2025-06-01T12:51:34.498966Z",
     "shell.execute_reply.started": "2025-06-01T12:51:34.448920Z"
    }
   },
   "outputs": [],
   "source": [
    "train_requests, val_requests = train_test_split(\n",
    "    sorted(list(final_filtered_requests)), test_size=0.02, random_state=42\n",
    ")\n",
    "print(len(train_requests), len(val_requests))\n",
    "\n",
    "train_df = df_slice[df_slice[\"request_id\"].isin(set(train_requests))].copy()\n",
    "val_df = df_slice[df_slice[\"request_id\"].isin(set(val_requests))].copy()\n",
    "train_df = train_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "train_df = train_df  # .reset_index()\n",
    "val_df = val_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "val_df = val_df  # .reset_index()\n",
    "\n",
    "print(train_df.shape, val_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:34.500151Z",
     "iopub.status.busy": "2025-06-01T12:51:34.500004Z",
     "iopub.status.idle": "2025-06-01T12:51:34.513583Z",
     "shell.execute_reply": "2025-06-01T12:51:34.513115Z",
     "shell.execute_reply.started": "2025-06-01T12:51:34.500135Z"
    }
   },
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:34.514823Z",
     "iopub.status.busy": "2025-06-01T12:51:34.514527Z",
     "iopub.status.idle": "2025-06-01T12:51:34.527423Z",
     "shell.execute_reply": "2025-06-01T12:51:34.526964Z",
     "shell.execute_reply.started": "2025-06-01T12:51:34.514807Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:34.528033Z",
     "iopub.status.busy": "2025-06-01T12:51:34.527899Z",
     "iopub.status.idle": "2025-06-01T12:51:35.100536Z",
     "shell.execute_reply": "2025-06-01T12:51:35.099986Z",
     "shell.execute_reply.started": "2025-06-01T12:51:34.528019Z"
    }
   },
   "outputs": [],
   "source": [
    "total_duration = 0\n",
    "for i, row in tqdm(train_df.iterrows(), total=len(train_df)):\n",
    "    # we need to alternate between preference: neg, pos\n",
    "    # print(i, row)\n",
    "    try:\n",
    "        assert row[\"preference\"] == (i % 2 == 1)\n",
    "    except:\n",
    "        print(i, row)\n",
    "    total_duration += row[\"duration\"]\n",
    "print(\n",
    "    f\"{round(total_duration / 60 / 60):,} hours of {train_df.shape[0]} clips, {train_df.shape[0] / 8 / 2 / 1000} nodes, {train_df.shape[0] / 8 / 2 / 4} steps\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:35.101241Z",
     "iopub.status.busy": "2025-06-01T12:51:35.101094Z",
     "iopub.status.idle": "2025-06-01T12:51:40.698853Z",
     "shell.execute_reply": "2025-06-01T12:51:40.698274Z",
     "shell.execute_reply.started": "2025-06-01T12:51:35.101226Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    val_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=True,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:51:40.699548Z",
     "iopub.status.busy": "2025-06-01T12:51:40.699397Z",
     "iopub.status.idle": "2025-06-01T12:56:01.793969Z",
     "shell.execute_reply": "2025-06-01T12:56:01.793286Z",
     "shell.execute_reply.started": "2025-06-01T12:51:40.699532Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    train_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=False,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-29T19:46:47.549860Z",
     "start_time": "2024-01-29T19:46:47.548015Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:01.794909Z",
     "iopub.status.busy": "2025-06-01T12:56:01.794645Z",
     "iopub.status.idle": "2025-06-01T12:56:01.969421Z",
     "shell.execute_reply": "2025-06-01T12:56:01.968768Z",
     "shell.execute_reply.started": "2025-06-01T12:56:01.794893Z"
    }
   },
   "outputs": [],
   "source": [
    "# verify\n",
    "metas_val = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "print(len(metas_val) / 2)\n",
    "mm_semantic_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_semantic_val.bin\"), dtype=np.uint16, mode=\"r\"\n",
    ")\n",
    "mm_vae_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    ")\n",
    "\n",
    "\n",
    "mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "print(mm_vae_val.shape)\n",
    "\n",
    "mm_semantic_val = mm_semantic_val.reshape(-1, SEMANTIC_MEMMAP_SIZE)\n",
    "print(mm_semantic_val.shape)\n",
    "\n",
    "assert len(metas_val) == mm_vae_val.shape[0] == mm_semantic_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:01.970259Z",
     "iopub.status.busy": "2025-06-01T12:56:01.970101Z",
     "iopub.status.idle": "2025-06-01T12:56:01.986911Z",
     "shell.execute_reply": "2025-06-01T12:56:01.986364Z",
     "shell.execute_reply.started": "2025-06-01T12:56:01.970244Z"
    }
   },
   "outputs": [],
   "source": [
    "# # load codec for decoding\n",
    "# from suno_utils.tasks.dac_vae_100hz_peaq import (  # NOTE: works for 25hz as well\n",
    "#     preload_models as preload_codec_models,\n",
    "#     decode as codec_decode,\n",
    "#     encode as codec_encode,\n",
    "#     get_embedding_rate,\n",
    "#     load_model as load_codec_model,\n",
    "# )\n",
    "\n",
    "# CODEC_FILEPATH = \"s3://suno-data/christian/25hz_vae_peaq_kl_0.005.pth\"\n",
    "# preload_codec_models(CODEC_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:01.988961Z",
     "iopub.status.busy": "2025-06-01T12:56:01.988651Z",
     "iopub.status.idle": "2025-06-01T12:56:02.004013Z",
     "shell.execute_reply": "2025-06-01T12:56:02.003466Z",
     "shell.execute_reply.started": "2025-06-01T12:56:01.988943Z"
    }
   },
   "outputs": [],
   "source": [
    "# # decode some audio\n",
    "idx = 108\n",
    "# # ensure even index\n",
    "assert idx % 2 == 0\n",
    "# print(metas_val[idx])\n",
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[idx])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(metas_val[idx + 1])\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[idx + 1])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:02.004861Z",
     "iopub.status.busy": "2025-06-01T12:56:02.004604Z",
     "iopub.status.idle": "2025-06-01T12:56:04.540965Z",
     "shell.execute_reply": "2025-06-01T12:56:04.540339Z",
     "shell.execute_reply.started": "2025-06-01T12:56:02.004846Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:04.541798Z",
     "iopub.status.busy": "2025-06-01T12:56:04.541633Z",
     "iopub.status.idle": "2025-06-01T12:56:04.585143Z",
     "shell.execute_reply": "2025-06-01T12:56:04.584699Z",
     "shell.execute_reply.started": "2025-06-01T12:56:04.541780Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:04.585778Z",
     "iopub.status.busy": "2025-06-01T12:56:04.585637Z",
     "iopub.status.idle": "2025-06-01T12:56:04.603183Z",
     "shell.execute_reply": "2025-06-01T12:56:04.602756Z",
     "shell.execute_reply.started": "2025-06-01T12:56:04.585763Z"
    }
   },
   "outputs": [],
   "source": [
    "# original_npz_path = f\"/app/suno/data/dpo/7b_npz/{test_metas[idx]['id']}.npz\"\n",
    "# original_npz_path = \"/app/suno/data/dpo/7b_npz/729c3011-f672-4ccd-8d82-1cbf2b52ff69.npz\"\n",
    "# original_arr = np.load(original_npz_path)[\"v2_raw\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:04.603900Z",
     "iopub.status.busy": "2025-06-01T12:56:04.603757Z",
     "iopub.status.idle": "2025-06-01T12:56:04.619138Z",
     "shell.execute_reply": "2025-06-01T12:56:04.618703Z",
     "shell.execute_reply.started": "2025-06-01T12:56:04.603885Z"
    }
   },
   "outputs": [],
   "source": [
    "def validation_on_metas(input_metas):\n",
    "    total_bad = 0\n",
    "    total_good = 0\n",
    "    for idx in range(len(input_metas)):\n",
    "        if idx % 2 == 0:\n",
    "            pos_idx = idx + 1\n",
    "            if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n",
    "                # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n",
    "                total_bad += 1\n",
    "            else:\n",
    "                total_good += 1\n",
    "    print(total_good, total_bad)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:04.619756Z",
     "iopub.status.busy": "2025-06-01T12:56:04.619614Z",
     "iopub.status.idle": "2025-06-01T12:56:05.523652Z",
     "shell.execute_reply": "2025-06-01T12:56:05.523052Z",
     "shell.execute_reply.started": "2025-06-01T12:56:04.619742Z"
    }
   },
   "outputs": [],
   "source": [
    "metas_tr = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_tr.jsonl\"))\n",
    "validation_on_metas(metas_tr)\n",
    "print(len(metas_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.524391Z",
     "iopub.status.busy": "2025-06-01T12:56:05.524233Z",
     "iopub.status.idle": "2025-06-01T12:56:05.552061Z",
     "shell.execute_reply": "2025-06-01T12:56:05.551606Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.524373Z"
    }
   },
   "outputs": [],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.552734Z",
     "iopub.status.busy": "2025-06-01T12:56:05.552591Z",
     "iopub.status.idle": "2025-06-01T12:56:05.571579Z",
     "shell.execute_reply": "2025-06-01T12:56:05.571144Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.552719Z"
    }
   },
   "outputs": [],
   "source": [
    "!cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion_infill.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.572191Z",
     "iopub.status.busy": "2025-06-01T12:56:05.572049Z",
     "iopub.status.idle": "2025-06-01T12:56:05.601814Z",
     "shell.execute_reply": "2025-06-01T12:56:05.601352Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.572177Z"
    }
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_upsample_v2_r1.ipynb\",\n",
    "    os.path.join(OUT_DATA_DIR, \"make_dataset.ipynb\"),\n",
    ")\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/preference_data_preparation_diff.py\",\n",
    "    os.path.join(OUT_DATA_DIR, \"preference_data_preparation_diff.py\"),\n",
    ")\n",
    "print(\"Cache kept!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.602524Z",
     "iopub.status.busy": "2025-06-01T12:56:05.602388Z",
     "iopub.status.idle": "2025-06-01T12:56:05.616977Z",
     "shell.execute_reply": "2025-06-01T12:56:05.616539Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.602510Z"
    }
   },
   "outputs": [],
   "source": [
    "# df[df[\"preference\"] & (df[\"shimmer_score_diff\"] > 3)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()\n",
    "\n",
    "# df[df[\"preference\"] & (df[\"pair_quality\"] < 0.1)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"pair_quality\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.617579Z",
     "iopub.status.busy": "2025-06-01T12:56:05.617444Z",
     "iopub.status.idle": "2025-06-01T12:56:05.631989Z",
     "shell.execute_reply": "2025-06-01T12:56:05.631562Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.617566Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_pair_df = df[df[\"request_id\"] == \"621c8b02-a905-48f1-a2d5-a4e8423d1505\"]\n",
    "# print(\n",
    "#     test_pair_df[\n",
    "#         [\n",
    "#             \"s3_id\",\n",
    "#             \"total_shimmer_score\",\n",
    "#             \"pair_quality\",\n",
    "#             \"request_id\",\n",
    "#             \"preference\",\n",
    "#             \"prompt_text\",\n",
    "#         ]\n",
    "#     ]\n",
    "# )\n",
    "# negative_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[0]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"negative\")\n",
    "# negative_audio.get_segment(0, 30).play()\n",
    "# positive_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[1]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"positive\")\n",
    "# positive_audio.get_segment(0, 30).play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.632582Z",
     "iopub.status.busy": "2025-06-01T12:56:05.632447Z",
     "iopub.status.idle": "2025-06-01T12:56:05.646695Z",
     "shell.execute_reply": "2025-06-01T12:56:05.646270Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.632568Z"
    }
   },
   "outputs": [],
   "source": [
    "# total_dict = {}\n",
    "# total_dict.update(pair_quality_dict)\n",
    "# total_dict.update(pair_quality_1_dict)\n",
    "# total_dict.update(pair_quality_2_dict)\n",
    "# total_dict.update(pair_quality_3_dict)\n",
    "# len(total_dict)\n",
    "# with open(\n",
    "#     os.path.join(\"/home/tony/Data/Preference/up_v1\", \"pair_quality.json\"), \"w\"\n",
    "# ) as fp:\n",
    "#     json.dump(total_dict, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.647300Z",
     "iopub.status.busy": "2025-06-01T12:56:05.647162Z",
     "iopub.status.idle": "2025-06-01T12:56:05.661344Z",
     "shell.execute_reply": "2025-06-01T12:56:05.660905Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.647286Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.661937Z",
     "iopub.status.busy": "2025-06-01T12:56:05.661806Z",
     "iopub.status.idle": "2025-06-01T12:56:05.675936Z",
     "shell.execute_reply": "2025-06-01T12:56:05.675514Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.661924Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_arr = np.load(\"/home/tony/Data/test_npz/diffusion_input_tensor([ 18, 182]).npy\")\n",
    "# test_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.676658Z",
     "iopub.status.busy": "2025-06-01T12:56:05.676520Z",
     "iopub.status.idle": "2025-06-01T12:56:05.690585Z",
     "shell.execute_reply": "2025-06-01T12:56:05.690166Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.676644Z"
    }
   },
   "outputs": [],
   "source": [
    "# mm_vae_val = np.memmap(\n",
    "#     os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    "# )\n",
    "\n",
    "# mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "# print(mm_vae_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.691135Z",
     "iopub.status.busy": "2025-06-01T12:56:05.691002Z",
     "iopub.status.idle": "2025-06-01T12:56:05.705143Z",
     "shell.execute_reply": "2025-06-01T12:56:05.704722Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.691121Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(test_arr[0].T / 2.5)\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(test_arr[1].T / 2.5)\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.705708Z",
     "iopub.status.busy": "2025-06-01T12:56:05.705576Z",
     "iopub.status.idle": "2025-06-01T12:56:05.719835Z",
     "shell.execute_reply": "2025-06-01T12:56:05.719419Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.705695Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[18])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[19])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.720440Z",
     "iopub.status.busy": "2025-06-01T12:56:05.720304Z",
     "iopub.status.idle": "2025-06-01T12:56:05.750322Z",
     "shell.execute_reply": "2025-06-01T12:56:05.749721Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.720426Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.752627Z",
     "iopub.status.busy": "2025-06-01T12:56:05.752469Z",
     "iopub.status.idle": "2025-06-01T12:56:05.771783Z",
     "shell.execute_reply": "2025-06-01T12:56:05.771307Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.752611Z"
    }
   },
   "outputs": [],
   "source": [
    "t = rng.draw(4)[:, 0].to(torch.bfloat16)\n",
    "print(t)\n",
    "# Replace 1% of t with ones to ensure training on terminal SNR\n",
    "t = torch.where(torch.rand_like(t) < 0.5, torch.ones_like(t), t)\n",
    "print(t)\n",
    "t = torch.repeat_interleave(t, repeats=2, dim=0)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.772404Z",
     "iopub.status.busy": "2025-06-01T12:56:05.772264Z",
     "iopub.status.idle": "2025-06-01T12:56:05.788171Z",
     "shell.execute_reply": "2025-06-01T12:56:05.787736Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.772390Z"
    }
   },
   "outputs": [],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.788784Z",
     "iopub.status.busy": "2025-06-01T12:56:05.788647Z",
     "iopub.status.idle": "2025-06-01T12:56:05.803237Z",
     "shell.execute_reply": "2025-06-01T12:56:05.802787Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.788770Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.803846Z",
     "iopub.status.busy": "2025-06-01T12:56:05.803706Z",
     "iopub.status.idle": "2025-06-01T12:56:05.819486Z",
     "shell.execute_reply": "2025-06-01T12:56:05.819055Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.803831Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.820184Z",
     "iopub.status.busy": "2025-06-01T12:56:05.820048Z",
     "iopub.status.idle": "2025-06-01T12:56:05.834435Z",
     "shell.execute_reply": "2025-06-01T12:56:05.833994Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.820170Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_diff2_v1//full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# # result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in range(24):\n",
    "#     with open(f\"/home/tony/Data/Preference/up_diff2_v1/full_pair_quality_{job_idx}.json\", \"r\") as fp:\n",
    "#         current_result = json.load(fp)\n",
    "#         result.update(current_result)\n",
    "# print(len(result))\n",
    "# with open(f\"/home/tony/Data/Preference/up_diff2_v1/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.835171Z",
     "iopub.status.busy": "2025-06-01T12:56:05.834915Z",
     "iopub.status.idle": "2025-06-01T12:56:05.849565Z",
     "shell.execute_reply": "2025-06-01T12:56:05.849127Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.835155Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# print(len(result))\n",
    "# print(len(result_loundess))\n",
    "# new_result = {}\n",
    "# for k, v in result.items():\n",
    "#     new_v = v.copy()\n",
    "#     for clip_id, contents in v.items():\n",
    "#         if contents and \"abs_loudness_factor\" not in contents:\n",
    "#             # print(contents, result_loundess[k][clip_id])\n",
    "#             try:\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] =  result_loundess[k][clip_id][\"abs_loudness_factor\"]\n",
    "#             except:\n",
    "#                 # print(clip_id)\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] = 0.0\n",
    "#             # break\n",
    "#     # print(k, v)\n",
    "#     # break\n",
    "#     new_result[k] = new_v\n",
    "# print(len(new_result))\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(new_result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.850282Z",
     "iopub.status.busy": "2025-06-01T12:56:05.850035Z",
     "iopub.status.idle": "2025-06-01T12:56:05.864390Z",
     "shell.execute_reply": "2025-06-01T12:56:05.863959Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.850267Z"
    }
   },
   "outputs": [],
   "source": [
    "# import torch\n",
    "# semantic_codes_chunk = torch.ones((1, 100))\n",
    "# semantic_skip_phase = 0\n",
    "# semantic_skip_factor = 4\n",
    "# mask = torch.ones_like(semantic_codes_chunk, dtype=torch.bool)\n",
    "# indices = (\n",
    "#     torch.arange(semantic_codes_chunk.size(1)) + semantic_skip_phase\n",
    "# ) % semantic_skip_factor == 0\n",
    "# mask[:, indices] = False\n",
    "# mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.864983Z",
     "iopub.status.busy": "2025-06-01T12:56:05.864846Z",
     "iopub.status.idle": "2025-06-01T12:56:05.879170Z",
     "shell.execute_reply": "2025-06-01T12:56:05.878738Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.864969Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.879886Z",
     "iopub.status.busy": "2025-06-01T12:56:05.879647Z",
     "iopub.status.idle": "2025-06-01T12:56:05.894042Z",
     "shell.execute_reply": "2025-06-01T12:56:05.893596Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.879871Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] < 0.5)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-01T12:56:05.894606Z",
     "iopub.status.busy": "2025-06-01T12:56:05.894475Z",
     "iopub.status.idle": "2025-06-01T12:56:05.908857Z",
     "shell.execute_reply": "2025-06-01T12:56:05.908418Z",
     "shell.execute_reply.started": "2025-06-01T12:56:05.894592Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] > 2)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  }
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